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Using Ontology-Based Context in the Portuguese-English Translation of
Homographs in Textual Dialogues | This paper introduces a novel approach to tackle the existing gap on message
translations in dialogue systems. Currently, submitted messages to the dialogue
systems are considered as isolated sentences. Thus, missing context information
impede the disambiguation of homographs words in ambiguous sentences. Our
approach solves this disambiguation problem by using concepts over existing
ontologies.
| 2,015 | Computation and Language |
Hierarchical Representation of Prosody for Statistical Speech Synthesis | Prominences and boundaries are the essential constituents of prosodic
structure in speech. They provide for means to chunk the speech stream into
linguistically relevant units by providing them with relative saliences and
demarcating them within coherent utterance structures. Prominences and
boundaries have both been widely used in both basic research on prosody as well
as in text-to-speech synthesis. However, there are no representation schemes
that would provide for both estimating and modelling them in a unified fashion.
Here we present an unsupervised unified account for estimating and representing
prosodic prominences and boundaries using a scale-space analysis based on
continuous wavelet transform. The methods are evaluated and compared to earlier
work using the Boston University Radio News corpus. The results show that the
proposed method is comparable with the best published supervised annotation
methods.
| 2,015 | Computation and Language |
Assisting Composition of Email Responses: a Topic Prediction Approach | We propose an approach for helping agents compose email replies to customer
requests. To enable that, we use LDA to extract latent topics from a collection
of email exchanges. We then use these latent topics to label our data,
obtaining a so-called "silver standard" topic labelling. We exploit this
labelled set to train a classifier to: (i) predict the topic distribution of
the entire agent's email response, based on features of the customer's email;
and (ii) predict the topic distribution of the next sentence in the agent's
reply, based on the customer's email features and on features of the agent's
current sentence. The experimental results on a large email collection from a
contact center in the tele- com domain show that the proposed ap- proach is
effective in predicting the best topic of the agent's next sentence. In 80% of
the cases, the correct topic is present among the top five recommended topics
(out of fifty possible ones). This shows the potential of this method to be
applied in an interactive setting, where the agent is presented a small list of
likely topics to choose from for the next sentence.
| 2,015 | Computation and Language |
Resolving References to Objects in Photographs using the
Words-As-Classifiers Model | A common use of language is to refer to visually present objects. Modelling
it in computers requires modelling the link between language and perception.
The "words as classifiers" model of grounded semantics views words as
classifiers of perceptual contexts, and composes the meaning of a phrase
through composition of the denotations of its component words. It was recently
shown to perform well in a game-playing scenario with a small number of object
types. We apply it to two large sets of real-world photographs that contain a
much larger variety of types and for which referring expressions are available.
Using a pre-trained convolutional neural network to extract image features, and
augmenting these with in-picture positional information, we show that the model
achieves performance competitive with the state of the art in a reference
resolution task (given expression, find bounding box of its referent), while,
as we argue, being conceptually simpler and more flexible.
| 2,016 | Computation and Language |
Automata networks for multi-party communication in the Naming Game | The Naming Game has been studied to explore the role of self-organization in
the development and negotiation of linguistic conventions. In this paper, we
define an automata networks approach to the Naming Game. Two problems are
faced: (1) the definition of an automata networks for multi-party communicative
interactions; and (2) the proof of convergence for three different orders in
which the individuals are updated (updating schemes). Finally, computer
simulations are explored in two-dimensional lattices with the purpose to
recover the main features of the Naming Game and to describe the dynamics under
different updating schemes.
| 2,016 | Computation and Language |
Mapping Unseen Words to Task-Trained Embedding Spaces | We consider the supervised training setting in which we learn task-specific
word embeddings. We assume that we start with initial embeddings learned from
unlabelled data and update them to learn task-specific embeddings for words in
the supervised training data. However, for new words in the test set, we must
use either their initial embeddings or a single unknown embedding, which often
leads to errors. We address this by learning a neural network to map from
initial embeddings to the task-specific embedding space, via a multi-loss
objective function. The technique is general, but here we demonstrate its use
for improved dependency parsing (especially for sentences with
out-of-vocabulary words), as well as for downstream improvements on sentiment
analysis.
| 2,016 | Computation and Language |
Controlled Experiments for Word Embeddings | An experimental approach to studying the properties of word embeddings is
proposed. Controlled experiments, achieved through modifications of the
training corpus, permit the demonstration of direct relations between word
properties and word vector direction and length. The approach is demonstrated
using the word2vec CBOW model with experiments that independently vary word
frequency and word co-occurrence noise. The experiments reveal that word vector
length depends more or less linearly on both word frequency and the level of
noise in the co-occurrence distribution of the word. The coefficients of
linearity depend upon the word. The special point in feature space, defined by
the (artificial) word with pure noise in its co-occurrence distribution, is
found to be small but non-zero.
| 2,015 | Computation and Language |
Human languages order information efficiently | Most languages use the relative order between words to encode meaning
relations. Languages differ, however, in what orders they use and how these
orders are mapped onto different meanings. We test the hypothesis that, despite
these differences, human languages might constitute different `solutions' to
common pressures of language use. Using Monte Carlo simulations over data from
five languages, we find that their word orders are efficient for processing in
terms of both dependency length and local lexical probability. This suggests
that biases originating in how the brain understands language strongly
constrain how human languages change over generations.
| 2,015 | Computation and Language |
OmniGraph: Rich Representation and Graph Kernel Learning | OmniGraph, a novel representation to support a range of NLP classification
tasks, integrates lexical items, syntactic dependencies and frame semantic
parses into graphs. Feature engineering is folded into the learning through
convolution graph kernel learning to explore different extents of the graph. A
high-dimensional space of features includes individual nodes as well as complex
subgraphs. In experiments on a text-forecasting problem that predicts stock
price change from news for company mentions, OmniGraph beats several benchmarks
based on bag-of-words, syntactic dependencies, and semantic trees. The highly
expressive features OmniGraph discovers provide insights into the semantics
across distinct market sectors. To demonstrate the method's generality, we also
report its high performance results on a fine-grained sentiment corpus.
| 2,015 | Computation and Language |
Textual Analysis for Studying Chinese Historical Documents and Literary
Novels | We analyzed historical and literary documents in Chinese to gain insights
into research issues, and overview our studies which utilized four different
sources of text materials in this paper. We investigated the history of
concepts and transliterated words in China with the Database for the Study of
Modern China Thought and Literature, which contains historical documents about
China between 1830 and 1930. We also attempted to disambiguate names that were
shared by multiple government officers who served between 618 and 1912 and were
recorded in Chinese local gazetteers. To showcase the potentials and challenges
of computer-assisted analysis of Chinese literatures, we explored some
interesting yet non-trivial questions about two of the Four Great Classical
Novels of China: (1) Which monsters attempted to consume the Buddhist monk
Xuanzang in the Journey to the West (JTTW), which was published in the 16th
century, (2) Which was the most powerful monster in JTTW, and (3) Which major
role smiled the most in the Dream of the Red Chamber, which was published in
the 18th century. Similar approaches can be applied to the analysis and study
of modern documents, such as the newspaper articles published about the 228
incident that occurred in 1947 in Taiwan.
| 2,015 | Computation and Language |
A Diversity-Promoting Objective Function for Neural Conversation Models | Sequence-to-sequence neural network models for generation of conversational
responses tend to generate safe, commonplace responses (e.g., "I don't know")
regardless of the input. We suggest that the traditional objective function,
i.e., the likelihood of output (response) given input (message) is unsuited to
response generation tasks. Instead we propose using Maximum Mutual Information
(MMI) as the objective function in neural models. Experimental results
demonstrate that the proposed MMI models produce more diverse, interesting, and
appropriate responses, yielding substantive gains in BLEU scores on two
conversational datasets and in human evaluations.
| 2,016 | Computation and Language |
Towards Meaningful Maps of Polish Case Law | In this work, we analyze the utility of two dimensional document maps for
exploratory analysis of Polish case law. We start by comparing two methods of
generating such visualizations. First is based on linear principal component
analysis (PCA). Second makes use of the modern nonlinear t-Distributed
Stochastic Neighbor Embedding method (t-SNE). We apply both PCA and t-SNE to a
corpus of judgments from different courts in Poland. It emerges that t-SNE
provides better, more interpretable results than PCA. As a next test, we apply
t-SNE to randomly selected sample of common court judgments corresponding to
different keywords. We show that t-SNE, in this case, reveals hidden topical
structure of the documents related to keyword,,pension". In conclusion, we find
that the t-SNE method could be a promising tool to facilitate the exploitative
analysis of legal texts, e.g., by complementing search or browse functionality
in legal databases.
| 2,016 | Computation and Language |
Bridge Correlational Neural Networks for Multilingual Multimodal
Representation Learning | Recently there has been a lot of interest in learning common representations
for multiple views of data. Typically, such common representations are learned
using a parallel corpus between the two views (say, 1M images and their English
captions). In this work, we address a real-world scenario where no direct
parallel data is available between two views of interest (say, $V_1$ and $V_2$)
but parallel data is available between each of these views and a pivot view
($V_3$). We propose a model for learning a common representation for $V_1$,
$V_2$ and $V_3$ using only the parallel data available between $V_1V_3$ and
$V_2V_3$. The proposed model is generic and even works when there are $n$ views
of interest and only one pivot view which acts as a bridge between them. There
are two specific downstream applications that we focus on (i) transfer learning
between languages $L_1$,$L_2$,...,$L_n$ using a pivot language $L$ and (ii)
cross modal access between images and a language $L_1$ using a pivot language
$L_2$. Our model achieves state-of-the-art performance in multilingual document
classification on the publicly available multilingual TED corpus and promising
results in multilingual multimodal retrieval on a new dataset created and
released as a part of this work.
| 2,016 | Computation and Language |
Hybrid Dialog State Tracker | This paper presents a hybrid dialog state tracker that combines a rule based
and a machine learning based approach to belief state tracking. Therefore, we
call it a hybrid tracker. The machine learning in our tracker is realized by a
Long Short Term Memory (LSTM) network. To our knowledge, our hybrid tracker
sets a new state-of-the-art result for the Dialog State Tracking Challenge
(DSTC) 2 dataset when the system uses only live SLU as its input.
| 2,016 | Computation and Language |
Improved Deep Learning Baselines for Ubuntu Corpus Dialogs | This paper presents results of our experiments for the next utterance ranking
on the Ubuntu Dialog Corpus -- the largest publicly available multi-turn dialog
corpus. First, we use an in-house implementation of previously reported models
to do an independent evaluation using the same data. Second, we evaluate the
performances of various LSTMs, Bi-LSTMs and CNNs on the dataset. Third, we
create an ensemble by averaging predictions of multiple models. The ensemble
further improves the performance and it achieves a state-of-the-art result for
the next utterance ranking on this dataset. Finally, we discuss our future
plans using this corpus.
| 2,015 | Computation and Language |
A Sensitivity Analysis of (and Practitioners' Guide to) Convolutional
Neural Networks for Sentence Classification | Convolutional Neural Networks (CNNs) have recently achieved remarkably strong
performance on the practically important task of sentence classification (kim
2014, kalchbrenner 2014, johnson 2014). However, these models require
practitioners to specify an exact model architecture and set accompanying
hyperparameters, including the filter region size, regularization parameters,
and so on. It is currently unknown how sensitive model performance is to
changes in these configurations for the task of sentence classification. We
thus conduct a sensitivity analysis of one-layer CNNs to explore the effect of
architecture components on model performance; our aim is to distinguish between
important and comparatively inconsequential design decisions for sentence
classification. We focus on one-layer CNNs (to the exclusion of more complex
models) due to their comparative simplicity and strong empirical performance,
which makes it a modern standard baseline method akin to Support Vector Machine
(SVMs) and logistic regression. We derive practical advice from our extensive
empirical results for those interested in getting the most out of CNNs for
sentence classification in real world settings.
| 2,016 | Computation and Language |
A Preliminary Study on the Learning Informativeness of Data Subsets | Estimating the internal state of a robotic system is complex: this is
performed from multiple heterogeneous sensor inputs and knowledge sources.
Discretization of such inputs is done to capture saliences, represented as
symbolic information, which often presents structure and recurrence. As these
sequences are used to reason over complex scenarios, a more compact
representation would aid exactness of technical cognitive reasoning
capabilities, which are today constrained by computational complexity issues
and fallback to representational heuristics or human intervention. Such
problems need to be addressed to ensure timely and meaningful human-robot
interaction. Our work is towards understanding the variability of learning
informativeness when training on subsets of a given input dataset. This is in
view of reducing the training size while retaining the majority of the symbolic
learning potential. We prove the concept on human-written texts, and conjecture
this work will reduce training data size of sequential instructions, while
preserving semantic relations, when gathering information from large remote
sources.
| 2,015 | Computation and Language |
Noisy-parallel and comparable corpora filtering methodology for the
extraction of bi-lingual equivalent data at sentence level | Text alignment and text quality are critical to the accuracy of Machine
Translation (MT) systems, some NLP tools, and any other text processing tasks
requiring bilingual data. This research proposes a language independent
bi-sentence filtering approach based on Polish (not a position-sensitive
language) to English experiments. This cleaning approach was developed on the
TED Talks corpus and also initially tested on the Wikipedia comparable corpus,
but it can be used for any text domain or language pair. The proposed approach
implements various heuristics for sentence comparison. Some of them leverage
synonyms and semantic and structural analysis of text as additional
information. Minimization of data loss was ensured. An improvement in MT system
score with text processed using the tool is discussed.
| 2,015 | Computation and Language |
Telemedicine as a special case of Machine Translation | Machine translation is evolving quite rapidly in terms of quality. Nowadays,
we have several machine translation systems available in the web, which provide
reasonable translations. However, these systems are not perfect, and their
quality may decrease in some specific domains. This paper examines the effects
of different training methods when it comes to Polish - English Statistical
Machine Translation system used for the medical data. Numerous elements of the
EMEA parallel text corpora and not related OPUS Open Subtitles project were
used as the ground for creation of phrase tables and different language models
including the development, tuning and testing of these translation systems. The
BLEU, NIST, METEOR, and TER metrics have been used in order to evaluate the
results of various systems. Our experiments deal with the systems that include
POS tagging, factored phrase models, hierarchical models, syntactic taggers,
and other alignment methods. We also executed a deep analysis of Polish data as
preparatory work before automatized data processing such as true casing or
punctuation normalization phase. Normalized metrics was used to compare
results. Scores lower than 15% mean that Machine Translation engine is unable
to provide satisfying quality, scores greater than 30% mean that translations
should be understandable without problems and scores over 50 reflect adequate
translations. The average results of Polish to English translations scores for
BLEU, NIST, METEOR, and TER were relatively high and ranged from 70,58 to
82,72. The lowest score was 64,38. The average results ranges for English to
Polish translations were little lower (67,58 - 78,97). The real-life
implementations of presented high quality Machine Translation Systems are
anticipated in general medical practice and telemedicine.
| 2,015 | Computation and Language |
Multilingual Image Description with Neural Sequence Models | In this paper we present an approach to multi-language image description
bringing together insights from neural machine translation and neural image
description. To create a description of an image for a given target language,
our sequence generation models condition on feature vectors from the image, the
description from the source language, and/or a multimodal vector computed over
the image and a description in the source language. In image description
experiments on the IAPR-TC12 dataset of images aligned with English and German
sentences, we find significant and substantial improvements in BLEU4 and Meteor
scores for models trained over multiple languages, compared to a monolingual
baseline.
| 2,015 | Computation and Language |
A Method for Modeling Co-Occurrence Propensity of Clinical Codes with
Application to ICD-10-PCS Auto-Coding | Objective. Natural language processing methods for medical auto-coding, or
automatic generation of medical billing codes from electronic health records,
generally assign each code independently of the others. They may thus assign
codes for closely related procedures or diagnoses to the same document, even
when they do not tend to occur together in practice, simply because the right
choice can be difficult to infer from the clinical narrative.
Materials and Methods. We propose a method that injects awareness of the
propensities for code co-occurrence into this process. First, a model is
trained to estimate the conditional probability that one code is assigned by a
human coder, given than another code is known to have been assigned to the same
document. Then, at runtime, an iterative algorithm is used to apply this model
to the output of an existing statistical auto-coder to modify the confidence
scores of the codes.
Results. We tested this method in combination with a primary auto-coder for
ICD-10 procedure codes, achieving a 12% relative improvement in F-score over
the primary auto-coder baseline.
Discussion. The proposed method can be used, with appropriate features, in
combination with any auto-coder that generates codes with different levels of
confidence.
Conclusion. The promising results obtained for ICD-10 procedure codes suggest
that the proposed method may have wider applications in auto-coding.
| 2,015 | Computation and Language |
A Graph Traversal Based Approach to Answer Non-Aggregation Questions
Over DBpedia | We present a question answering system over DBpedia, filling the gap between
user information needs expressed in natural language and a structured query
interface expressed in SPARQL over the underlying knowledge base (KB). Given
the KB, our goal is to comprehend a natural language query and provide
corresponding accurate answers. Focusing on solving the non-aggregation
questions, in this paper, we construct a subgraph of the knowledge base from
the detected entities and propose a graph traversal method to solve both the
semantic item mapping problem and the disambiguation problem in a joint way.
Compared with existing work, we simplify the process of query intention
understanding and pay more attention to the answer path ranking. We evaluate
our method on a non-aggregation question dataset and further on a complete
dataset. Experimental results show that our method achieves best performance
compared with several state-of-the-art systems.
| 2,018 | Computation and Language |
Normalization of Relative and Incomplete Temporal Expressions in
Clinical Narratives | We analyze the RI-TIMEXes in temporally annotated corpora and propose two
hypotheses regarding the normalization of RI-TIMEXes in the clinical narrative
domain: the anchor point hypothesis and the anchor relation hypothesis. We
annotate the RI-TIMEXes in three corpora to study the characteristics of
RI-TMEXes in different domains. This informed the design of our RI-TIMEX
normalization system for the clinical domain, which consists of an anchor point
classifier, an anchor relation classifier and a rule-based RI-TIMEX text span
parser. We experiment with different feature sets and perform error analysis
for each system component. The annotation confirmed the hypotheses that we can
simplify the RI-TIMEXes normalization task using two multi-label classifiers.
Our system achieves anchor point classification, anchor relation classification
and rule-based parsing accuracy of 74.68%, 87.71% and 57.2% (82.09% under
relaxed matching criteria) respectively on the held-out test set of the 2012
i2b2 temporal relation challenge. Experiments with feature sets reveals some
interesting findings such as the verbal tense feature does not inform the
anchor relation classification in clinical narratives as much as the tokens
near the RI-TIMEX. Error analysis shows that underrepresented anchor point and
anchor relation classes are difficult to detect. We formulate the RI-TIMEX
normalization problem as a pair of multi-label classification problems.
Considering only the RI-TIMEX extraction and normalization, the system achieves
statistically significant improvement over the RI-TIMEX results of the best
systems in the 2012 i2b2 challenge.
| 2,015 | Computation and Language |
Neural Reranking Improves Subjective Quality of Machine Translation:
NAIST at WAT2015 | This year, the Nara Institute of Science and Technology (NAIST)'s submission
to the 2015 Workshop on Asian Translation was based on syntax-based statistical
machine translation, with the addition of a reranking component using neural
attentional machine translation models. Experiments re-confirmed results from
previous work stating that neural MT reranking provides a large gain in
objective evaluation measures such as BLEU, and also confirmed for the first
time that these results also carry over to manual evaluation. We further
perform a detailed analysis of reasons for this increase, finding that the main
contributions of the neural models lie in improvement of the grammatical
correctness of the output, as opposed to improvements in lexical choice of
content words.
| 2,015 | Computation and Language |
Part-of-Speech Tagging with Bidirectional Long Short-Term Memory
Recurrent Neural Network | Bidirectional Long Short-Term Memory Recurrent Neural Network (BLSTM-RNN) has
been shown to be very effective for tagging sequential data, e.g. speech
utterances or handwritten documents. While word embedding has been demoed as a
powerful representation for characterizing the statistical properties of
natural language. In this study, we propose to use BLSTM-RNN with word
embedding for part-of-speech (POS) tagging task. When tested on Penn Treebank
WSJ test set, a state-of-the-art performance of 97.40 tagging accuracy is
achieved. Without using morphological features, this approach can also achieve
a good performance comparable with the Stanford POS tagger.
| 2,015 | Computation and Language |
Prevalence and recoverability of syntactic parameters in sparse
distributed memories | We propose a new method, based on Sparse Distributed Memory (Kanerva
Networks), for studying dependency relations between different syntactic
parameters in the Principles and Parameters model of Syntax. We store data of
syntactic parameters of world languages in a Kanerva Network and we check the
recoverability of corrupted parameter data from the network. We find that
different syntactic parameters have different degrees of recoverability. We
identify two different effects: an overall underlying relation between the
prevalence of parameters across languages and their degree of recoverability,
and a finer effect that makes some parameters more easily recoverable beyond
what their prevalence would indicate. We interpret a higher recoverability for
a syntactic parameter as an indication of the existence of a dependency
relation, through which the given parameter can be determined using the
remaining uncorrupted data.
| 2,015 | Computation and Language |
Multi-GPU Distributed Parallel Bayesian Differential Topic Modelling | There is an explosion of data, documents, and other content, and people
require tools to analyze and interpret these, tools to turn the content into
information and knowledge. Topic modeling have been developed to solve these
problems. Topic models such as LDA [Blei et. al. 2003] allow salient patterns
in data to be extracted automatically. When analyzing texts, these patterns are
called topics. Among numerous extensions of LDA, few of them can reliably
analyze multiple groups of documents and extract topic similarities. Recently,
the introduction of differential topic modeling (SPDP) [Chen et. al. 2012]
performs uniformly better than many topic models in a discriminative setting.
There is also a need to improve the sampling speed for topic models. While
some effort has been made for distributed algorithms, there is no work
currently done using graphical processing units (GPU). Note the GPU framework
has already become the most cost-efficient platform for many problems.
In this thesis, I propose and implement a scalable multi-GPU distributed
parallel framework which approximates SPDP. Through experiments, I have shown
my algorithms have a gain in speed of about 50 times while being almost as
accurate, with only one single cheap laptop GPU. Furthermore, I have shown the
speed improvement is sublinearly scalable when multiple GPUs are used, while
fairly maintaining the accuracy. Therefore on a medium-sized GPU cluster, the
speed improvement could potentially reach a factor of a thousand.
Note SPDP is just a representative of other extensions of LDA. Although my
algorithm is implemented to work with SPDP, it is designed to be a general
enough to work with other topic models. The speed-up on smaller collections
(i.e., 1000s of documents), means that these more complex LDA extensions could
now be done in real-time, thus opening up a new way of using these LDA models
in industry.
| 2,015 | Computation and Language |
Freshman or Fresher? Quantifying the Geographic Variation of Internet
Language | We present a new computational technique to detect and analyze statistically
significant geographic variation in language. Our meta-analysis approach
captures statistical properties of word usage across geographical regions and
uses statistical methods to identify significant changes specific to regions.
While previous approaches have primarily focused on lexical variation between
regions, our method identifies words that demonstrate semantic and syntactic
variation as well.
We extend recently developed techniques for neural language models to learn
word representations which capture differing semantics across geographical
regions. In order to quantify this variation and ensure robust detection of
true regional differences, we formulate a null model to determine whether
observed changes are statistically significant. Our method is the first such
approach to explicitly account for random variation due to chance while
detecting regional variation in word meaning.
To validate our model, we study and analyze two different massive online data
sets: millions of tweets from Twitter spanning not only four different
countries but also fifty states, as well as millions of phrases contained in
the Google Book Ngrams. Our analysis reveals interesting facets of language
change at multiple scales of geographic resolution -- from neighboring states
to distant continents.
Finally, using our model, we propose a measure of semantic distance between
languages. Our analysis of British and American English over a period of 100
years reveals that semantic variation between these dialects is shrinking.
| 2,016 | Computation and Language |
Learning in the Rational Speech Acts Model | The Rational Speech Acts (RSA) model treats language use as a recursive
process in which probabilistic speaker and listener agents reason about each
other's intentions to enrich the literal semantics of their language along
broadly Gricean lines. RSA has been shown to capture many kinds of
conversational implicature, but it has been criticized as an unrealistic model
of speakers, and it has so far required the manual specification of a semantic
lexicon, preventing its use in natural language processing applications that
learn lexical knowledge from data. We address these concerns by showing how to
define and optimize a trained statistical classifier that uses the intermediate
agents of RSA as hidden layers of representation forming a non-linear
activation function. This treatment opens up new application domains and new
possibilities for learning effectively from data. We validate the model on a
referential expression generation task, showing that the best performance is
achieved by incorporating features approximating well-established insights
about natural language generation into RSA.
| 2,015 | Computation and Language |
Combine CRF and MMSEG to Boost Chinese Word Segmentation in Social Media | In this paper, we propose a joint algorithm for the word segmentation on
Chinese social media. Previous work mainly focus on word segmentation for plain
Chinese text, in order to develop a Chinese social media processing tool, we
need to take the main features of social media into account, whose grammatical
structure is not rigorous, and the tendency of using colloquial and Internet
terms makes the existing Chinese-processing tools inefficient to obtain good
performance on social media.
In our approach, we combine CRF and MMSEG algorithm and extend features of
traditional CRF algorithm to train the model for word segmentation, We use
Internet lexicon in order to improve the performance of our model on Chinese
social media. Our experimental result on Sina Weibo shows that our approach
outperforms the state-of-the-art model.
| 2,015 | Computation and Language |
Statistical Parsing by Machine Learning from a Classical Arabic Treebank | Research into statistical parsing for English has enjoyed over a decade of
successful results. However, adapting these models to other languages has met
with difficulties. Previous comparative work has shown that Modern Arabic is
one of the most difficult languages to parse due to rich morphology and free
word order. Classical Arabic is the ancient form of Arabic, and is understudied
in computational linguistics, relative to its worldwide reach as the language
of the Quran. The thesis is based on seven publications that make significant
contributions to knowledge relating to annotating and parsing Classical Arabic.
A central argument of this thesis is that using a hybrid representation
closely aligned to traditional grammar leads to improved parsing for Arabic. To
test this hypothesis, two approaches are compared. As a reference, a pure
dependency parser is adapted using graph transformations, resulting in an
87.47% F1-score. This is compared to an integrated parsing model with an
F1-score of 89.03%, demonstrating that joint dependency-constituency parsing is
better suited to Classical Arabic.
| 2,015 | Computation and Language |
How to merge three different methods for information filtering ? | Twitter is now a gold marketing tool for entities concerned with online
reputation. To automatically monitor online reputation of entities , systems
have to deal with ambiguous entity names, polarity detection and topic
detection. We propose three approaches to tackle the first issue: monitoring
Twitter in order to find relevant tweets about a given entity. Evaluated within
the framework of the RepLab-2013 Filtering task, each of them has been shown
competitive with state-of-the-art approaches. Mainly we investigate on how much
merging strategies may impact performances on a filtering task according to the
evaluation measure.
| 2,015 | Computation and Language |
Edge-Linear First-Order Dependency Parsing with Undirected Minimum
Spanning Tree Inference | The run time complexity of state-of-the-art inference algorithms in
graph-based dependency parsing is super-linear in the number of input words
(n). Recently, pruning algorithms for these models have shown to cut a large
portion of the graph edges, with minimal damage to the resulting parse trees.
Solving the inference problem in run time complexity determined solely by the
number of edges (m) is hence of obvious importance.
We propose such an inference algorithm for first-order models, which encodes
the problem as a minimum spanning tree (MST) problem in an undirected graph.
This allows us to utilize state-of-the-art undirected MST algorithms whose run
time is O(m) at expectation and with a very high probability. A directed parse
tree is then inferred from the undirected MST and is subsequently improved with
respect to the directed parsing model through local greedy updates, both steps
running in O(n) time. In experiments with 18 languages, a variant of the
first-order MSTParser (McDonald et al., 2005b) that employs our algorithm
performs very similarly to the original parser that runs an O(n^2) directed MST
inference.
| 2,016 | Computation and Language |
Empirical Study on Deep Learning Models for Question Answering | In this paper we explore deep learning models with memory component or
attention mechanism for question answering task. We combine and compare three
models, Neural Machine Translation, Neural Turing Machine, and Memory Networks
for a simulated QA data set. This paper is the first one that uses Neural
Machine Translation and Neural Turing Machines for solving QA tasks. Our
results suggest that the combination of attention and memory have potential to
solve certain QA problem.
| 2,015 | Computation and Language |
Parser for Abstract Meaning Representation using Learning to Search | We develop a novel technique to parse English sentences into Abstract Meaning
Representation (AMR) using SEARN, a Learning to Search approach, by modeling
the concept and the relation learning in a unified framework. We evaluate our
parser on multiple datasets from varied domains and show an absolute
improvement of 2% to 6% over the state-of-the-art. Additionally we show that
using the most frequent concept gives us a baseline that is stronger than the
state-of-the-art for concept prediction. We plan to release our parser for
public use.
| 2,015 | Computation and Language |
Standards for language resources in ISO -- Looking back at 13 fruitful
years | This paper provides an overview of the various projects carried out within
ISO committee TC 37/SC 4 dealing with the management of language (digital)
resources. On the basis of the technical experience gained in the committee and
the wider standardization landscape the paper identifies some possible trends
for the future.
| 2,015 | Computation and Language |
Fast k-best Sentence Compression | A popular approach to sentence compression is to formulate the task as a
constrained optimization problem and solve it with integer linear programming
(ILP) tools. Unfortunately, dependence on ILP may make the compressor
prohibitively slow, and thus approximation techniques have been proposed which
are often complex and offer a moderate gain in speed. As an alternative
solution, we introduce a novel compression algorithm which generates k-best
compressions relying on local deletion decisions. Our algorithm is two orders
of magnitude faster than a recent ILP-based method while producing better
compressions. Moreover, an extensive evaluation demonstrates that the quality
of compressions does not degrade much as we move from single best to top-five
results.
| 2,015 | Computation and Language |
Emoticons vs. Emojis on Twitter: A Causal Inference Approach | Online writing lacks the non-verbal cues present in face-to-face
communication, which provide additional contextual information about the
utterance, such as the speaker's intention or affective state. To fill this
void, a number of orthographic features, such as emoticons, expressive
lengthening, and non-standard punctuation, have become popular in social media
services including Twitter and Instagram. Recently, emojis have been introduced
to social media, and are increasingly popular. This raises the question of
whether these predefined pictographic characters will come to replace earlier
orthographic methods of paralinguistic communication. In this abstract, we
attempt to shed light on this question, using a matching approach from causal
inference to test whether the adoption of emojis causes individual users to
employ fewer emoticons in their text on Twitter.
| 2,015 | Computation and Language |
Prediction-Adaptation-Correction Recurrent Neural Networks for
Low-Resource Language Speech Recognition | In this paper, we investigate the use of prediction-adaptation-correction
recurrent neural networks (PAC-RNNs) for low-resource speech recognition. A
PAC-RNN is comprised of a pair of neural networks in which a {\it correction}
network uses auxiliary information given by a {\it prediction} network to help
estimate the state probability. The information from the correction network is
also used by the prediction network in a recurrent loop. Our model outperforms
other state-of-the-art neural networks (DNNs, LSTMs) on IARPA-Babel tasks.
Moreover, transfer learning from a language that is similar to the target
language can help improve performance further.
| 2,018 | Computation and Language |
SentiWords: Deriving a High Precision and High Coverage Lexicon for
Sentiment Analysis | Deriving prior polarity lexica for sentiment analysis - where positive or
negative scores are associated with words out of context - is a challenging
task. Usually, a trade-off between precision and coverage is hard to find, and
it depends on the methodology used to build the lexicon. Manually annotated
lexica provide a high precision but lack in coverage, whereas automatic
derivation from pre-existing knowledge guarantees high coverage at the cost of
a lower precision. Since the automatic derivation of prior polarities is less
time consuming than manual annotation, there has been a great bloom of these
approaches, in particular based on the SentiWordNet resource. In this paper, we
compare the most frequently used techniques based on SentiWordNet with newer
ones and blend them in a learning framework (a so called 'ensemble method'). By
taking advantage of manually built prior polarity lexica, our ensemble method
is better able to predict the prior value of unseen words and to outperform all
the other SentiWordNet approaches. Using this technique we have built
SentiWords, a prior polarity lexicon of approximately 155,000 words, that has
both a high precision and a high coverage. We finally show that in sentiment
analysis tasks, using our lexicon allows us to outperform both the single
metrics derived from SentiWordNet and popular manually annotated sentiment
lexica.
| 2,015 | Computation and Language |
Generating Text with Deep Reinforcement Learning | We introduce a novel schema for sequence to sequence learning with a Deep
Q-Network (DQN), which decodes the output sequence iteratively. The aim here is
to enable the decoder to first tackle easier portions of the sequences, and
then turn to cope with difficult parts. Specifically, in each iteration, an
encoder-decoder Long Short-Term Memory (LSTM) network is employed to, from the
input sequence, automatically create features to represent the internal states
of and formulate a list of potential actions for the DQN. Take rephrasing a
natural sentence as an example. This list can contain ranked potential words.
Next, the DQN learns to make decision on which action (e.g., word) will be
selected from the list to modify the current decoded sequence. The newly
modified output sequence is subsequently used as the input to the DQN for the
next decoding iteration. In each iteration, we also bias the reinforcement
learning's attention to explore sequence portions which are previously
difficult to be decoded. For evaluation, the proposed strategy was trained to
decode ten thousands natural sentences. Our experiments indicate that, when
compared to a left-to-right greedy beam search LSTM decoder, the proposed
method performed competitively well when decoding sentences from the training
set, but significantly outperformed the baseline when decoding unseen
sentences, in terms of BLEU score obtained.
| 2,015 | Computation and Language |
Top-down Tree Long Short-Term Memory Networks | Long Short-Term Memory (LSTM) networks, a type of recurrent neural network
with a more complex computational unit, have been successfully applied to a
variety of sequence modeling tasks. In this paper we develop Tree Long
Short-Term Memory (TreeLSTM), a neural network model based on LSTM, which is
designed to predict a tree rather than a linear sequence. TreeLSTM defines the
probability of a sentence by estimating the generation probability of its
dependency tree. At each time step, a node is generated based on the
representation of the generated sub-tree. We further enhance the modeling power
of TreeLSTM by explicitly representing the correlations between left and right
dependents. Application of our model to the MSR sentence completion challenge
achieves results beyond the current state of the art. We also report results on
dependency parsing reranking achieving competitive performance.
| 2,016 | Computation and Language |
A Unified Tagging Solution: Bidirectional LSTM Recurrent Neural Network
with Word Embedding | Bidirectional Long Short-Term Memory Recurrent Neural Network (BLSTM-RNN) has
been shown to be very effective for modeling and predicting sequential data,
e.g. speech utterances or handwritten documents. In this study, we propose to
use BLSTM-RNN for a unified tagging solution that can be applied to various
tagging tasks including part-of-speech tagging, chunking and named entity
recognition. Instead of exploiting specific features carefully optimized for
each task, our solution only uses one set of task-independent features and
internal representations learnt from unlabeled text for all tasks.Requiring no
task specific knowledge or sophisticated feature engineering, our approach gets
nearly state-of-the-art performance in all these three tagging tasks.
| 2,015 | Computation and Language |
Automatic Prosody Prediction for Chinese Speech Synthesis using
BLSTM-RNN and Embedding Features | Prosody affects the naturalness and intelligibility of speech. However,
automatic prosody prediction from text for Chinese speech synthesis is still a
great challenge and the traditional conditional random fields (CRF) based
method always heavily relies on feature engineering. In this paper, we propose
to use neural networks to predict prosodic boundary labels directly from
Chinese characters without any feature engineering. Experimental results show
that stacking feed-forward and bidirectional long short-term memory (BLSTM)
recurrent network layers achieves superior performance over the CRF-based
method. The embedding features learned from raw text further enhance the
performance.
| 2,015 | Computation and Language |
Detecting Interrogative Utterances with Recurrent Neural Networks | In this paper, we explore different neural network architectures that can
predict if a speaker of a given utterance is asking a question or making a
statement. We com- pare the outcomes of regularization methods that are
popularly used to train deep neural networks and study how different context
functions can affect the classification performance. We also compare the
efficacy of gated activation functions that are favorably used in recurrent
neural networks and study how to combine multimodal inputs. We evaluate our
models on two multimodal datasets: MSR-Skype and CALLHOME.
| 2,015 | Computation and Language |
Mining Local Gazetteers of Literary Chinese with CRF and Pattern based
Methods for Biographical Information in Chinese History | Person names and location names are essential building blocks for identifying
events and social networks in historical documents that were written in
literary Chinese. We take the lead to explore the research on algorithmically
recognizing named entities in literary Chinese for historical studies with
language-model based and conditional-random-field based methods, and extend our
work to mining the document structures in historical documents. Practical
evaluations were conducted with texts that were extracted from more than 220
volumes of local gazetteers (Difangzhi). Difangzhi is a huge and the single
most important collection that contains information about officers who served
in local government in Chinese history. Our methods performed very well on
these realistic tests. Thousands of names and addresses were identified from
the texts. A good portion of the extracted names match the biographical
information currently recorded in the China Biographical Database (CBDB) of
Harvard University, and many others can be verified by historians and will
become as new additions to CBDB.
| 2,016 | Computation and Language |
Color Aesthetics and Social Networks in Complete Tang Poems:
Explorations and Discoveries | The Complete Tang Poems (CTP) is the most important source to study Tang
poems. We look into CTP with computational tools from specific linguistic
perspectives, including distributional semantics and collocational analysis.
From such quantitative viewpoints, we compare the usage of "wind" and "moon" in
the poems of Li Bai and Du Fu. Colors in poems function like sounds in movies,
and play a crucial role in the imageries of poems. Thus, words for colors are
studied, and "white" is the main focus because it is the most frequent color in
CTP. We also explore some cases of using colored words in antithesis pairs that
were central for fostering the imageries of the poems. CTP also contains useful
historical information, and we extract person names in CTP to study the social
networks of the Tang poets. Such information can then be integrated with the
China Biographical Database of Harvard University.
| 2,015 | Computation and Language |
Multinomial Loss on Held-out Data for the Sparse Non-negative Matrix
Language Model | We describe Sparse Non-negative Matrix (SNM) language model estimation using
multinomial loss on held-out data.
Being able to train on held-out data is important in practical situations
where the training data is usually mismatched from the held-out/test data. It
is also less constrained than the previous training algorithm using
leave-one-out on training data: it allows the use of richer meta-features in
the adjustment model, e.g. the diversity counts used by Kneser-Ney smoothing
which would be difficult to deal with correctly in leave-one-out training.
In experiments on the one billion words language modeling benchmark, we are
able to slightly improve on our previous results which use a different loss
function, and employ leave-one-out training on a subset of the main training
set. Surprisingly, an adjustment model with meta-features that discard all
lexical information can perform as well as lexicalized meta-features. We find
that fairly small amounts of held-out data (on the order of 30-70 thousand
words) are sufficient for training the adjustment model.
In a real-life scenario where the training data is a mix of data sources that
are imbalanced in size, and of different degrees of relevance to the held-out
and test data, taking into account the data source for a given skip-/n-gram
feature and combining them for best performance on held-out/test data improves
over skip-/n-gram SNM models trained on pooled data by about 8% in the SMT
setup, or as much as 15% in the ASR/IME setup.
The ability to mix various data sources based on how relevant they are to a
mismatched held-out set is probably the most attractive feature of the new
estimation method for SNM LM.
| 2,016 | Computation and Language |
An Empirical Study on Sentiment Classification of Chinese Review using
Word Embedding | In this article, how word embeddings can be used as features in Chinese
sentiment classification is presented. Firstly, a Chinese opinion corpus is
built with a million comments from hotel review websites. Then the word
embeddings which represent each comment are used as input in different machine
learning methods for sentiment classification, including SVM, Logistic
Regression, Convolutional Neural Network (CNN) and ensemble methods. These
methods get better performance compared with N-gram models using Naive Bayes
(NB) and Maximum Entropy (ME). Finally, a combination of machine learning
methods is proposed which presents an outstanding performance in precision,
recall and F1 score. After selecting the most useful methods to construct the
combinational model and testing over the corpus, the final F1 score is 0.920.
| 2,015 | Computation and Language |
Comparing Writing Styles using Word Embedding and Dynamic Time Warping | The development of plot or story in novels is reflected in the content and
the words used. The flow of sentiments, which is one aspect of writing style,
can be quantified by analyzing the flow of words. This study explores literary
works as signals in word embedding space and tries to compare writing styles of
popular classic novels using dynamic time warping.
| 2,015 | Computation and Language |
"Pale as death" or "p\^ale comme la mort" : Frozen similes used as
literary clich\'es | The present study is focused on the automatic identification and description
of frozen similes in British and French novels written between the 19 th
century and the beginning of the 20 th century. Two main patterns of frozen
similes were considered: adjectival ground + simile marker + nominal vehicle
(e.g. happy as a lark) and eventuality + simile marker + nominal vehicle (e.g.
sleep like a top). All potential similes and their components were first
extracted using a rule-based algorithm. Then, frozen similes were identified
based on reference lists of existing similes and semantic distance between the
tenor and the vehicle. The results obtained tend to confirm the fact that
frozen similes are not used haphazardly in literary texts. In addition,
contrary to how they are often presented, frozen similes often go beyond the
ground or the eventuality and the vehicle to also include the tenor.
| 2,016 | Computation and Language |
Multi-lingual Geoparsing based on Machine Translation | Our method for multi-lingual geoparsing uses monolingual tools and resources
along with machine translation and alignment to return location words in many
languages. Not only does our method save the time and cost of developing
geoparsers for each language separately, but also it allows the possibility of
a wide range of language capabilities within a single interface. We evaluated
our method in our LanguageBridge prototype on location named entities using
newswire, broadcast news and telephone conversations in English, Arabic and
Chinese data from the Linguistic Data Consortium (LDC). Our results for
geoparsing Chinese and Arabic text using our multi-lingual geoparsing method
are comparable to our results for geoparsing English text with our English
tools. Furthermore, experiments using our machine translation approach results
in accuracy comparable to results from the same data that was translated
manually.
| 2,015 | Computation and Language |
Population size predicts lexical diversity, but so does the mean sea
level - why it is important to correctly account for the structure of
temporal data | In order to demonstrate why it is important to correctly account for the
(serial dependent) structure of temporal data, we document an apparently
spectacular relationship between population size and lexical diversity: for
five out of seven investigated languages, there is a strong relationship
between population size and lexical diversity of the primary language in this
country. We show that this relationship is the result of a misspecified model
that does not consider the temporal aspect of the data by presenting a similar
but nonsensical relationship between the global annual mean sea level and
lexical diversity. Given the fact that in the recent past, several studies were
published that present surprising links between different economic, cultural,
political and (socio-)demographical variables on the one hand and cultural or
linguistic characteristics on the other hand, but seem to suffer from exactly
this problem, we explain the cause of the misspecification and show that it has
profound consequences. We demonstrate how simple transformation of the time
series can often solve problems of this type and argue that the evaluation of
the plausibility of a relationship is important in this context. We hope that
our paper will help both researchers and reviewers to understand why it is
important to use special models for the analysis of data with a natural
temporal ordering.
| 2,016 | Computation and Language |
Introducing SKYSET - a Quintuple Approach for Improving Instructions | A new approach called SKYSET (Synthetic Knowledge Yield Social Entities
Translation) is proposed to validate completeness and to reduce ambiguity from
written instructional documentation. SKYSET utilizes a quintuple set of
standardized categories, which differs from traditional approaches that
typically use triples. The SKYSET System defines the categories required to
form a standard template for representing information that is portable across
different domains. It provides a standardized framework that enables sentences
from written instructions to be translated into sets of category typed entities
on a table or database. The SKYSET entities contain conceptual units or phrases
that represent information from the original source documentation. SKYSET
enables information concatenation where multiple documents from different
domains can be translated and combined into a single common filterable and
searchable table of entities.
| 2,015 | Computation and Language |
The Goldilocks Principle: Reading Children's Books with Explicit Memory
Representations | We introduce a new test of how well language models capture meaning in
children's books. Unlike standard language modelling benchmarks, it
distinguishes the task of predicting syntactic function words from that of
predicting lower-frequency words, which carry greater semantic content. We
compare a range of state-of-the-art models, each with a different way of
encoding what has been previously read. We show that models which store
explicit representations of long-term contexts outperform state-of-the-art
neural language models at predicting semantic content words, although this
advantage is not observed for syntactic function words. Interestingly, we find
that the amount of text encoded in a single memory representation is highly
influential to the performance: there is a sweet-spot, not too big and not too
small, between single words and full sentences that allows the most meaningful
information in a text to be effectively retained and recalled. Further, the
attention over such window-based memories can be trained effectively through
self-supervision. We then assess the generality of this principle by applying
it to the CNN QA benchmark, which involves identifying named entities in
paraphrased summaries of news articles, and achieve state-of-the-art
performance.
| 2,016 | Computation and Language |
Review-Level Sentiment Classification with Sentence-Level Polarity
Correction | We propose an effective technique to solving review-level sentiment
classification problem by using sentence-level polarity correction. Our
polarity correction technique takes into account the consistency of the
polarities (positive and negative) of sentences within each product review
before performing the actual machine learning task. While sentences with
inconsistent polarities are removed, sentences with consistent polarities are
used to learn state-of-the-art classifiers. The technique achieved better
results on different types of products reviews and outperforms baseline models
without the correction technique. Experimental results show an average of 82%
F-measure on four different product review domains.
| 2,015 | Computation and Language |
A Chinese POS Decision Method Using Korean Translation Information | In this paper we propose a method that imitates a translation expert using
the Korean translation information and analyse the performance. Korean is good
at tagging than Chinese, so we can use this property in Chinese POS tagging.
| 2,015 | Computation and Language |
Learning Linguistic Biomarkers for Predicting Mild Cognitive Impairment
using Compound Skip-grams | Predicting Mild Cognitive Impairment (MCI) is currently a challenge as
existing diagnostic criteria rely on neuropsychological examinations. Automated
Machine Learning (ML) models that are trained on verbal utterances of MCI
patients can aid diagnosis. Using a combination of skip-gram features, our
model learned several linguistic biomarkers to distinguish between 19 patients
with MCI and 19 healthy control individuals from the DementiaBank language
transcript clinical dataset. Results show that a model with compound of
skip-grams has better AUC and could help ML prediction on small MCI data
sample.
| 2,015 | Computation and Language |
Towards Structured Deep Neural Network for Automatic Speech Recognition | In this paper we propose the Structured Deep Neural Network (structured DNN)
as a structured and deep learning framework. This approach can learn to find
the best structured object (such as a label sequence) given a structured input
(such as a vector sequence) by globally considering the mapping relationships
between the structures rather than item by item.
When automatic speech recognition is viewed as a special case of such a
structured learning problem, where we have the acoustic vector sequence as the
input and the phoneme label sequence as the output, it becomes possible to
comprehensively learn utterance by utterance as a whole, rather than frame by
frame.
Structured Support Vector Machine (structured SVM) was proposed to perform
ASR with structured learning previously, but limited by the linear nature of
SVM. Here we propose structured DNN to use nonlinear transformations in
multi-layers as a structured and deep learning approach. This approach was
shown to beat structured SVM in preliminary experiments on TIMIT.
| 2,015 | Computation and Language |
Sentiment Expression via Emoticons on Social Media | Emoticons (e.g., :) and :( ) have been widely used in sentiment analysis and
other NLP tasks as features to ma- chine learning algorithms or as entries of
sentiment lexicons. In this paper, we argue that while emoticons are strong and
common signals of sentiment expression on social media the relationship between
emoticons and sentiment polarity are not always clear. Thus, any algorithm that
deals with sentiment polarity should take emoticons into account but extreme
cau- tion should be exercised in which emoticons to depend on. First, to
demonstrate the prevalence of emoticons on social media, we analyzed the
frequency of emoticons in a large re- cent Twitter data set. Then we carried
out four analyses to examine the relationship between emoticons and sentiment
polarity as well as the contexts in which emoticons are used. The first
analysis surveyed a group of participants for their perceived sentiment
polarity of the most frequent emoticons. The second analysis examined
clustering of words and emoti- cons to better understand the meaning conveyed
by the emoti- cons. The third analysis compared the sentiment polarity of
microblog posts before and after emoticons were removed from the text. The last
analysis tested the hypothesis that removing emoticons from text hurts
sentiment classification by training two machine learning models with and
without emoticons in the text respectively. The results confirms the arguments
that: 1) a few emoticons are strong and reliable signals of sentiment polarity
and one should take advantage of them in any senti- ment analysis; 2) a large
group of the emoticons conveys com- plicated sentiment hence they should be
treated with extreme caution.
| 2,015 | Computation and Language |
Information retrieval in folktales using natural language processing | Our aim is to extract information about literary characters in unstructured
texts. We employ natural language processing and reasoning on domain
ontologies. The first task is to identify the main characters and the parts of
the story where these characters are described or act. We illustrate the system
in a scenario in the folktale domain. The system relies on a folktale ontology
that we have developed based on Propp's model for folktales morphology.
| 2,016 | Computation and Language |
Investigating the stylistic relevance of adjective and verb simile
markers | Similes play an important role in literary texts not only as rhetorical
devices and as figures of speech but also because of their evocative power,
their aptness for description and the relative ease with which they can be
combined with other figures of speech (Israel et al. 2004). Detecting all types
of simile constructions in a particular text therefore seems crucial when
analysing the style of an author. Few research studies however have been
dedicated to the study of less prominent simile markers in fictional prose and
their relevance for stylistic studies. The present paper studies the frequency
of adjective and verb simile markers in a corpus of British and French novels
in order to determine which ones are really informative and worth including in
a stylistic analysis. Furthermore, are those adjectives and verb simile markers
used differently in both languages?
| 2,015 | Computation and Language |
USFD: Twitter NER with Drift Compensation and Linked Data | This paper describes a pilot NER system for Twitter, comprising the USFD
system entry to the W-NUT 2015 NER shared task. The goal is to correctly label
entities in a tweet dataset, using an inventory of ten types. We employ
structured learning, drawing on gazetteers taken from Linked Data, and on
unsupervised clustering features, and attempting to compensate for stylistic
and topic drift - a key challenge in social media text. Our result is
competitive; we provide an analysis of the components of our methodology, and
an examination of the target dataset in the context of this task.
| 2,015 | Computation and Language |
Generative Concatenative Nets Jointly Learn to Write and Classify
Reviews | A recommender system's basic task is to estimate how users will respond to
unseen items. This is typically modeled in terms of how a user might rate a
product, but here we aim to extend such approaches to model how a user would
write about the product. To do so, we design a character-level Recurrent Neural
Network (RNN) that generates personalized product reviews. The network
convincingly learns styles and opinions of nearly 1000 distinct authors, using
a large corpus of reviews from BeerAdvocate.com. It also tailors reviews to
describe specific items, categories, and star ratings. Using a simple input
replication strategy, the Generative Concatenative Network (GCN) preserves the
signal of static auxiliary inputs across wide sequence intervals. Without any
additional training, the generative model can classify reviews, identifying the
author of the review, the product category, and the sentiment (rating), with
remarkable accuracy. Our evaluation shows the GCN captures complex dynamics in
text, such as the effect of negation, misspellings, slang, and large
vocabularies gracefully absent any machinery explicitly dedicated to the
purpose.
| 2,016 | Computation and Language |
Larger-Context Language Modelling | In this work, we propose a novel method to incorporate corpus-level discourse
information into language modelling. We call this larger-context language
model. We introduce a late fusion approach to a recurrent language model based
on long short-term memory units (LSTM), which helps the LSTM unit keep
intra-sentence dependencies and inter-sentence dependencies separate from each
other. Through the evaluation on three corpora (IMDB, BBC, and PennTree Bank),
we demon- strate that the proposed model improves perplexity significantly. In
the experi- ments, we evaluate the proposed approach while varying the number
of context sentences and observe that the proposed late fusion is superior to
the usual way of incorporating additional inputs to the LSTM. By analyzing the
trained larger- context language model, we discover that content words,
including nouns, adjec- tives and verbs, benefit most from an increasing number
of context sentences. This analysis suggests that larger-context language model
improves the unconditional language model by capturing the theme of a document
better and more easily.
| 2,015 | Computation and Language |
A Multilingual FrameNet-based Grammar and Lexicon for Controlled Natural
Language | Berkeley FrameNet is a lexico-semantic resource for English based on the
theory of frame semantics. It has been exploited in a range of natural language
processing applications and has inspired the development of framenets for many
languages. We present a methodological approach to the extraction and
generation of a computational multilingual FrameNet-based grammar and lexicon.
The approach leverages FrameNet-annotated corpora to automatically extract a
set of cross-lingual semantico-syntactic valence patterns. Based on data from
Berkeley FrameNet and Swedish FrameNet, the proposed approach has been
implemented in Grammatical Framework (GF), a categorial grammar formalism
specialized for multilingual grammars. The implementation of the grammar and
lexicon is supported by the design of FrameNet, providing a frame semantic
abstraction layer, an interlingual semantic API (application programming
interface), over the interlingual syntactic API already provided by GF Resource
Grammar Library. The evaluation of the acquired grammar and lexicon shows the
feasibility of the approach. Additionally, we illustrate how the FrameNet-based
grammar and lexicon are exploited in two distinct multilingual controlled
natural language applications. The produced resources are available under an
open source license.
| 2,017 | Computation and Language |
Document Context Language Models | Text documents are structured on multiple levels of detail: individual words
are related by syntax, but larger units of text are related by discourse
structure. Existing language models generally fail to account for discourse
structure, but it is crucial if we are to have language models that reward
coherence and generate coherent texts. We present and empirically evaluate a
set of multi-level recurrent neural network language models, called
Document-Context Language Models (DCLM), which incorporate contextual
information both within and beyond the sentence. In comparison with word-level
recurrent neural network language models, the DCLM models obtain slightly
better predictive likelihoods, and considerably better assessments of document
coherence.
| 2,016 | Computation and Language |
Multimodal Skip-gram Using Convolutional Pseudowords | This work studies the representational mapping across multimodal data such
that given a piece of the raw data in one modality the corresponding semantic
description in terms of the raw data in another modality is immediately
obtained. Such a representational mapping can be found in a wide spectrum of
real-world applications including image/video retrieval, object recognition,
action/behavior recognition, and event understanding and prediction. To that
end, we introduce a simplified training objective for learning multimodal
embeddings using the skip-gram architecture by introducing convolutional
"pseudowords:" embeddings composed of the additive combination of distributed
word representations and image features from convolutional neural networks
projected into the multimodal space. We present extensive results of the
representational properties of these embeddings on various word similarity
benchmarks to show the promise of this approach.
| 2,015 | Computation and Language |
LSTM-based Deep Learning Models for Non-factoid Answer Selection | In this paper, we apply a general deep learning (DL) framework for the answer
selection task, which does not depend on manually defined features or
linguistic tools. The basic framework is to build the embeddings of questions
and answers based on bidirectional long short-term memory (biLSTM) models, and
measure their closeness by cosine similarity. We further extend this basic
model in two directions. One direction is to define a more composite
representation for questions and answers by combining convolutional neural
network with the basic framework. The other direction is to utilize a simple
but efficient attention mechanism in order to generate the answer
representation according to the question context. Several variations of models
are provided. The models are examined by two datasets, including TREC-QA and
InsuranceQA. Experimental results demonstrate that the proposed models
substantially outperform several strong baselines.
| 2,016 | Computation and Language |
Character-based Neural Machine Translation | We introduce a neural machine translation model that views the input and
output sentences as sequences of characters rather than words. Since word-level
information provides a crucial source of bias, our input model composes
representations of character sequences into representations of words (as
determined by whitespace boundaries), and then these are translated using a
joint attention/translation model. In the target language, the translation is
modeled as a sequence of word vectors, but each word is generated one character
at a time, conditional on the previous character generations in each word. As
the representation and generation of words is performed at the character level,
our model is capable of interpreting and generating unseen word forms. A
secondary benefit of this approach is that it alleviates much of the challenges
associated with preprocessing/tokenization of the source and target languages.
We show that our model can achieve translation results that are on par with
conventional word-based models.
| 2,015 | Computation and Language |
Learning to Represent Words in Context with Multilingual Supervision | We present a neural network architecture based on bidirectional LSTMs to
compute representations of words in the sentential contexts. These
context-sensitive word representations are suitable for, e.g., distinguishing
different word senses and other context-modulated variations in meaning. To
learn the parameters of our model, we use cross-lingual supervision,
hypothesizing that a good representation of a word in context will be one that
is sufficient for selecting the correct translation into a second language. We
evaluate the quality of our representations as features in three downstream
tasks: prediction of semantic supersenses (which assign nouns and verbs into a
few dozen semantic classes), low resource machine translation, and a lexical
substitution task, and obtain state-of-the-art results on all of these.
| 2,015 | Computation and Language |
Word Embedding based Correlation Model for Question/Answer Matching | With the development of community based question answering (Q&A) services, a
large scale of Q&A archives have been accumulated and are an important
information and knowledge resource on the web. Question and answer matching has
been attached much importance to for its ability to reuse knowledge stored in
these systems: it can be useful in enhancing user experience with recurrent
questions. In this paper, we try to improve the matching accuracy by overcoming
the lexical gap between question and answer pairs. A Word Embedding based
Correlation (WEC) model is proposed by integrating advantages of both the
translation model and word embedding, given a random pair of words, WEC can
score their co-occurrence probability in Q&A pairs and it can also leverage the
continuity and smoothness of continuous space word representation to deal with
new pairs of words that are rare in the training parallel text. An experimental
study on Yahoo! Answers dataset and Baidu Zhidao dataset shows this new
method's promising potential.
| 2,017 | Computation and Language |
A System for Extracting Sentiment from Large-Scale Arabic Social Data | Social media data in Arabic language is becoming more and more abundant. It
is a consensus that valuable information lies in social media data. Mining this
data and making the process easier are gaining momentum in the industries. This
paper describes an enterprise system we developed for extracting sentiment from
large volumes of social data in Arabic dialects. First, we give an overview of
the Big Data system for information extraction from multilingual social data
from a variety of sources. Then, we focus on the Arabic sentiment analysis
capability that was built on top of the system including normalizing written
Arabic dialects, building sentiment lexicons, sentiment classification, and
performance evaluation. Lastly, we demonstrate the value of enriching sentiment
results with user profiles in understanding sentiments of a specific user
group.
| 2,015 | Computation and Language |
Learning Representations of Affect from Speech | There has been a lot of prior work on representation learning for speech
recognition applications, but not much emphasis has been given to an
investigation of effective representations of affect from speech, where the
paralinguistic elements of speech are separated out from the verbal content. In
this paper, we explore denoising autoencoders for learning paralinguistic
attributes i.e. categorical and dimensional affective traits from speech. We
show that the representations learnt by the bottleneck layer of the autoencoder
are highly discriminative of activation intensity and at separating out
negative valence (sadness and anger) from positive valence (happiness). We
experiment with different input speech features (such as FFT and log-mel
spectrograms with temporal context windows), and different autoencoder
architectures (such as stacked and deep autoencoders). We also learn utterance
specific representations by a combination of denoising autoencoders and BLSTM
based recurrent autoencoders. Emotion classification is performed with the
learnt temporal/dynamic representations to evaluate the quality of the
representations. Experiments on a well-established real-life speech dataset
(IEMOCAP) show that the learnt representations are comparable to state of the
art feature extractors (such as voice quality features and MFCCs) and are
competitive with state-of-the-art approaches at emotion and dimensional affect
recognition.
| 2,016 | Computation and Language |
Latent Dirichlet Allocation Based Organisation of Broadcast Media
Archives for Deep Neural Network Adaptation | This paper presents a new method for the discovery of latent domains in
diverse speech data, for the use of adaptation of Deep Neural Networks (DNNs)
for Automatic Speech Recognition. Our work focuses on transcription of
multi-genre broadcast media, which is often only categorised broadly in terms
of high level genres such as sports, news, documentary, etc. However, in terms
of acoustic modelling these categories are coarse. Instead, it is expected that
a mixture of latent domains can better represent the complex and diverse
behaviours within a TV show, and therefore lead to better and more robust
performance. We propose a new method, whereby these latent domains are
discovered with Latent Dirichlet Allocation, in an unsupervised manner. These
are used to adapt DNNs using the Unique Binary Code (UBIC) representation for
the LDA domains. Experiments conducted on a set of BBC TV broadcasts, with more
than 2,000 shows for training and 47 shows for testing, show that the use of
LDA-UBIC DNNs reduces the error up to 13% relative compared to the baseline
hybrid DNN models.
| 2,016 | Computation and Language |
Yin and Yang: Balancing and Answering Binary Visual Questions | The complex compositional structure of language makes problems at the
intersection of vision and language challenging. But language also provides a
strong prior that can result in good superficial performance, without the
underlying models truly understanding the visual content. This can hinder
progress in pushing state of art in the computer vision aspects of multi-modal
AI. In this paper, we address binary Visual Question Answering (VQA) on
abstract scenes. We formulate this problem as visual verification of concepts
inquired in the questions. Specifically, we convert the question to a tuple
that concisely summarizes the visual concept to be detected in the image. If
the concept can be found in the image, the answer to the question is "yes", and
otherwise "no". Abstract scenes play two roles (1) They allow us to focus on
the high-level semantics of the VQA task as opposed to the low-level
recognition problems, and perhaps more importantly, (2) They provide us the
modality to balance the dataset such that language priors are controlled, and
the role of vision is essential. In particular, we collect fine-grained pairs
of scenes for every question, such that the answer to the question is "yes" for
one scene, and "no" for the other for the exact same question. Indeed, language
priors alone do not perform better than chance on our balanced dataset.
Moreover, our proposed approach matches the performance of a state-of-the-art
VQA approach on the unbalanced dataset, and outperforms it on the balanced
dataset.
| 2,016 | Computation and Language |
Learning to retrieve out-of-vocabulary words in speech recognition | Many Proper Names (PNs) are Out-Of-Vocabulary (OOV) words for speech
recognition systems used to process diachronic audio data. To help recovery of
the PNs missed by the system, relevant OOV PNs can be retrieved out of the many
OOVs by exploiting semantic context of the spoken content. In this paper, we
propose two neural network models targeted to retrieve OOV PNs relevant to an
audio document: (a) Document level Continuous Bag of Words (D-CBOW), (b)
Document level Continuous Bag of Weighted Words (D-CBOW2). Both these models
take document words as input and learn with an objective to maximise the
retrieval of co-occurring OOV PNs. With the D-CBOW2 model we propose a new
approach in which the input embedding layer is augmented with a context anchor
layer. This layer learns to assign importance to input words and has the
ability to capture (task specific) key-words in a bag-of-word neural network
model. With experiments on French broadcast news videos we show that these two
models outperform the baseline methods based on raw embeddings from LDA,
Skip-gram and Paragraph Vectors. Combining the D-CBOW and D-CBOW2 models gives
faster convergence during training.
| 2,016 | Computation and Language |
Combining Neural Networks and Log-linear Models to Improve Relation
Extraction | The last decade has witnessed the success of the traditional feature-based
method on exploiting the discrete structures such as words or lexical patterns
to extract relations from text. Recently, convolutional and recurrent neural
networks has provided very effective mechanisms to capture the hidden
structures within sentences via continuous representations, thereby
significantly advancing the performance of relation extraction. The advantage
of convolutional neural networks is their capacity to generalize the
consecutive k-grams in the sentences while recurrent neural networks are
effective to encode long ranges of sentence context. This paper proposes to
combine the traditional feature-based method, the convolutional and recurrent
neural networks to simultaneously benefit from their advantages. Our systematic
evaluation of different network architectures and combination methods
demonstrates the effectiveness of this approach and results in the
state-of-the-art performance on the ACE 2005 and SemEval dataset.
| 2,015 | Computation and Language |
Segmental Recurrent Neural Networks | We introduce segmental recurrent neural networks (SRNNs) which define, given
an input sequence, a joint probability distribution over segmentations of the
input and labelings of the segments. Representations of the input segments
(i.e., contiguous subsequences of the input) are computed by encoding their
constituent tokens using bidirectional recurrent neural nets, and these
"segment embeddings" are used to define compatibility scores with output
labels. These local compatibility scores are integrated using a global
semi-Markov conditional random field. Both fully supervised training -- in
which segment boundaries and labels are observed -- as well as partially
supervised training -- in which segment boundaries are latent -- are
straightforward. Experiments on handwriting recognition and joint Chinese word
segmentation/POS tagging show that, compared to models that do not explicitly
represent segments such as BIO tagging schemes and connectionist temporal
classification (CTC), SRNNs obtain substantially higher accuracies.
| 2,016 | Computation and Language |
Neural Variational Inference for Text Processing | Recent advances in neural variational inference have spawned a renaissance in
deep latent variable models. In this paper we introduce a generic variational
inference framework for generative and conditional models of text. While
traditional variational methods derive an analytic approximation for the
intractable distributions over latent variables, here we construct an inference
network conditioned on the discrete text input to provide the variational
distribution. We validate this framework on two very different text modelling
applications, generative document modelling and supervised question answering.
Our neural variational document model combines a continuous stochastic document
representation with a bag-of-words generative model and achieves the lowest
reported perplexities on two standard test corpora. The neural answer selection
model employs a stochastic representation layer within an attention mechanism
to extract the semantics between a question and answer pair. On two question
answering benchmarks this model exceeds all previous published benchmarks.
| 2,016 | Computation and Language |
Overcoming Language Variation in Sentiment Analysis with Social
Attention | Variation in language is ubiquitous, particularly in newer forms of writing
such as social media. Fortunately, variation is not random, it is often linked
to social properties of the author. In this paper, we show how to exploit
social networks to make sentiment analysis more robust to social language
variation. The key idea is linguistic homophily: the tendency of socially
linked individuals to use language in similar ways. We formalize this idea in a
novel attention-based neural network architecture, in which attention is
divided among several basis models, depending on the author's position in the
social network. This has the effect of smoothing the classification function
across the social network, and makes it possible to induce personalized
classifiers even for authors for whom there is no labeled data or demographic
metadata. This model significantly improves the accuracies of sentiment
analysis on Twitter and on review data.
| 2,017 | Computation and Language |
Transfer Learning for Speech and Language Processing | Transfer learning is a vital technique that generalizes models trained for
one setting or task to other settings or tasks. For example in speech
recognition, an acoustic model trained for one language can be used to
recognize speech in another language, with little or no re-training data.
Transfer learning is closely related to multi-task learning (cross-lingual vs.
multilingual), and is traditionally studied in the name of `model adaptation'.
Recent advance in deep learning shows that transfer learning becomes much
easier and more effective with high-level abstract features learned by deep
models, and the `transfer' can be conducted not only between data distributions
and data types, but also between model structures (e.g., shallow nets and deep
nets) or even model types (e.g., Bayesian models and neural models). This
review paper summarizes some recent prominent research towards this direction,
particularly for speech and language processing. We also report some results
from our group and highlight the potential of this very interesting research
field.
| 2,015 | Computation and Language |
Knowledge Base Population using Semantic Label Propagation | A crucial aspect of a knowledge base population system that extracts new
facts from text corpora, is the generation of training data for its relation
extractors. In this paper, we present a method that maximizes the effectiveness
of newly trained relation extractors at a minimal annotation cost. Manual
labeling can be significantly reduced by Distant Supervision, which is a method
to construct training data automatically by aligning a large text corpus with
an existing knowledge base of known facts. For example, all sentences
mentioning both 'Barack Obama' and 'US' may serve as positive training
instances for the relation born_in(subject,object). However, distant
supervision typically results in a highly noisy training set: many training
sentences do not really express the intended relation. We propose to combine
distant supervision with minimal manual supervision in a technique called
feature labeling, to eliminate noise from the large and noisy initial training
set, resulting in a significant increase of precision. We further improve on
this approach by introducing the Semantic Label Propagation method, which uses
the similarity between low-dimensional representations of candidate training
instances, to extend the training set in order to increase recall while
maintaining high precision. Our proposed strategy for generating training data
is studied and evaluated on an established test collection designed for
knowledge base population tasks. The experimental results show that the
Semantic Label Propagation strategy leads to substantial performance gains when
compared to existing approaches, while requiring an almost negligible manual
annotation effort.
| 2,016 | Computation and Language |
Gaussian Mixture Embeddings for Multiple Word Prototypes | Recently, word representation has been increasingly focused on for its
excellent properties in representing the word semantics. Previous works mainly
suffer from the problem of polysemy phenomenon. To address this problem, most
of previous models represent words as multiple distributed vectors. However, it
cannot reflect the rich relations between words by representing words as points
in the embedded space. In this paper, we propose the Gaussian mixture skip-gram
(GMSG) model to learn the Gaussian mixture embeddings for words based on
skip-gram framework. Each word can be regarded as a gaussian mixture
distribution in the embedded space, and each gaussian component represents a
word sense. Since the number of senses varies from word to word, we further
propose the Dynamic GMSG (D-GMSG) model by adaptively increasing the sense
number of words during training. Experiments on four benchmarks show the
effectiveness of our proposed model.
| 2,015 | Computation and Language |
Harvesting comparable corpora and mining them for equivalent bilingual
sentences using statistical classification and analogy- based heuristics | Parallel sentences are a relatively scarce but extremely useful resource for
many applications including cross-lingual retrieval and statistical machine
translation. This research explores our new methodologies for mining such data
from previously obtained comparable corpora. The task is highly practical since
non-parallel multilingual data exist in far greater quantities than parallel
corpora, but parallel sentences are a much more useful resource. Here we
propose a web crawling method for building subject-aligned comparable corpora
from e.g. Wikipedia dumps and Euronews web page. The improvements in machine
translation are shown on Polish-English language pair for various text domains.
We also tested another method of building parallel corpora based on comparable
corpora data. It lets automatically broad existing corpus of sentences from
subject of corpora based on analogies between them.
| 2,015 | Computation and Language |
Good, Better, Best: Choosing Word Embedding Context | We propose two methods of learning vector representations of words and
phrases that each combine sentence context with structural features extracted
from dependency trees. Using several variations of neural network classifier,
we show that these combined methods lead to improved performance when used as
input features for supervised term-matching.
| 2,015 | Computation and Language |
Communicating Semantics: Reference by Description | Messages often refer to entities such as people, places and events. Correct
identification of the intended reference is an essential part of communication.
Lack of shared unique names often complicates entity reference. Shared
knowledge can be used to construct uniquely identifying descriptive references
for entities with ambiguous names. We introduce a mathematical model for
`Reference by Description', derive results on the conditions under which, with
high probability, programs can construct unambiguous references to most
entities in the domain of discourse and provide empirical validation of these
results.
| 2,017 | Computation and Language |
Dynamic Adaptive Network Intelligence | Accurate representational learning of both the explicit and implicit
relationships within data is critical to the ability of machines to perform
more complex and abstract reasoning tasks. We describe the efficient weakly
supervised learning of such inferences by our Dynamic Adaptive Network
Intelligence (DANI) model. We report state-of-the-art results for DANI over
question answering tasks in the bAbI dataset that have proved difficult for
contemporary approaches to learning representation (Weston et al., 2015).
| 2,015 | Computation and Language |
sense2vec - A Fast and Accurate Method for Word Sense Disambiguation In
Neural Word Embeddings | Neural word representations have proven useful in Natural Language Processing
(NLP) tasks due to their ability to efficiently model complex semantic and
syntactic word relationships. However, most techniques model only one
representation per word, despite the fact that a single word can have multiple
meanings or "senses". Some techniques model words by using multiple vectors
that are clustered based on context. However, recent neural approaches rarely
focus on the application to a consuming NLP algorithm. Furthermore, the
training process of recent word-sense models is expensive relative to
single-sense embedding processes. This paper presents a novel approach which
addresses these concerns by modeling multiple embeddings for each word based on
supervised disambiguation, which provides a fast and accurate way for a
consuming NLP model to select a sense-disambiguated embedding. We demonstrate
that these embeddings can disambiguate both contrastive senses such as nominal
and verbal senses as well as nuanced senses such as sarcasm. We further
evaluate Part-of-Speech disambiguated embeddings on neural dependency parsing,
yielding a greater than 8% average error reduction in unlabeled attachment
scores across 6 languages.
| 2,015 | Computation and Language |
Multilingual Relation Extraction using Compositional Universal Schema | Universal schema builds a knowledge base (KB) of entities and relations by
jointly embedding all relation types from input KBs as well as textual patterns
expressing relations from raw text. In most previous applications of universal
schema, each textual pattern is represented as a single embedding, preventing
generalization to unseen patterns. Recent work employs a neural network to
capture patterns' compositional semantics, providing generalization to all
possible input text. In response, this paper introduces significant further
improvements to the coverage and flexibility of universal schema relation
extraction: predictions for entities unseen in training and multilingual
transfer learning to domains with no annotation. We evaluate our model through
extensive experiments on the English and Spanish TAC KBP benchmark,
outperforming the top system from TAC 2013 slot-filling using no handwritten
patterns or additional annotation. We also consider a multilingual setting in
which English training data entities overlap with the seed KB, but Spanish text
does not. Despite having no annotation for Spanish data, we train an accurate
predictor, with additional improvements obtained by tying word embeddings
across languages. Furthermore, we find that multilingual training improves
English relation extraction accuracy. Our approach is thus suited to
broad-coverage automated knowledge base construction in a variety of languages
and domains.
| 2,016 | Computation and Language |
Compressing Word Embeddings | Recent methods for learning vector space representations of words have
succeeded in capturing fine-grained semantic and syntactic regularities using
vector arithmetic. However, these vector space representations (created through
large-scale text analysis) are typically stored verbatim, since their internal
structure is opaque. Using word-analogy tests to monitor the level of detail
stored in compressed re-representations of the same vector space, the
trade-offs between the reduction in memory usage and expressiveness are
investigated. A simple scheme is outlined that can reduce the memory footprint
of a state-of-the-art embedding by a factor of 10, with only minimal impact on
performance. Then, using the same `bit budget', a binary (approximate)
factorisation of the same space is also explored, with the aim of creating an
equivalent representation with better interpretability.
| 2,016 | Computation and Language |
Reasoning in Vector Space: An Exploratory Study of Question Answering | Question answering tasks have shown remarkable progress with distributed
vector representation. In this paper, we investigate the recently proposed
Facebook bAbI tasks which consist of twenty different categories of questions
that require complex reasoning. Because the previous work on bAbI are all
end-to-end models, errors could come from either an imperfect understanding of
semantics or in certain steps of the reasoning. For clearer analysis, we
propose two vector space models inspired by Tensor Product Representation (TPR)
to perform knowledge encoding and logical reasoning based on common-sense
inference. They together achieve near-perfect accuracy on all categories
including positional reasoning and path finding that have proved difficult for
most of the previous approaches. We hypothesize that the difficulties in these
categories are due to the multi-relations in contrast to uni-relational
characteristic of other categories. Our exploration sheds light on designing
more sophisticated dataset and moving one step toward integrating transparent
and interpretable formalism of TPR into existing learning paradigms.
| 2,016 | Computation and Language |
Joint Word Representation Learning using a Corpus and a Semantic Lexicon | Methods for learning word representations using large text corpora have
received much attention lately due to their impressive performance in numerous
natural language processing (NLP) tasks such as, semantic similarity
measurement, and word analogy detection. Despite their success, these
data-driven word representation learning methods do not consider the rich
semantic relational structure between words in a co-occurring context. On the
other hand, already much manual effort has gone into the construction of
semantic lexicons such as the WordNet that represent the meanings of words by
defining the various relationships that exist among the words in a language. We
consider the question, can we improve the word representations learnt using a
corpora by integrating the knowledge from semantic lexicons?. For this purpose,
we propose a joint word representation learning method that simultaneously
predicts the co-occurrences of two words in a sentence subject to the
relational constrains given by the semantic lexicon. We use relations that
exist between words in the lexicon to regularize the word representations
learnt from the corpus. Our proposed method statistically significantly
outperforms previously proposed methods for incorporating semantic lexicons
into word representations on several benchmark datasets for semantic similarity
and word analogy.
| 2,016 | Computation and Language |
Polysemy in Controlled Natural Language Texts | Computational semantics and logic-based controlled natural languages (CNL) do
not address systematically the word sense disambiguation problem of content
words, i.e., they tend to interpret only some functional words that are crucial
for construction of discourse representation structures. We show that
micro-ontologies and multi-word units allow integration of the rich and
polysemous multi-domain background knowledge into CNL thus providing
interpretation for the content words. The proposed approach is demonstrated by
extending the Attempto Controlled English (ACE) with polysemous and procedural
constructs resulting in a more natural CNL named PAO covering narrative
multi-domain texts.
| 2,010 | Computation and Language |
Improving Neural Machine Translation Models with Monolingual Data | Neural Machine Translation (NMT) has obtained state-of-the art performance
for several language pairs, while only using parallel data for training.
Target-side monolingual data plays an important role in boosting fluency for
phrase-based statistical machine translation, and we investigate the use of
monolingual data for NMT. In contrast to previous work, which combines NMT
models with separately trained language models, we note that encoder-decoder
NMT architectures already have the capacity to learn the same information as a
language model, and we explore strategies to train with monolingual data
without changing the neural network architecture. By pairing monolingual
training data with an automatic back-translation, we can treat it as additional
parallel training data, and we obtain substantial improvements on the WMT 15
task English<->German (+2.8-3.7 BLEU), and for the low-resourced IWSLT 14 task
Turkish->English (+2.1-3.4 BLEU), obtaining new state-of-the-art results. We
also show that fine-tuning on in-domain monolingual and parallel data gives
substantial improvements for the IWSLT 15 task English->German.
| 2,016 | Computation and Language |
Conducting sparse feature selection on arbitrarily long phrases in text
corpora with a focus on interpretability | We propose a general framework for topic-specific summarization of large text
corpora, and illustrate how it can be used for analysis in two quite different
contexts: an OSHA database of fatality and catastrophe reports (to facilitate
surveillance for patterns in circumstances leading to injury or death) and
legal decisions on workers' compensation claims (to explore relevant case law).
Our summarization framework, built on sparse classification methods, is a
compromise between simple word frequency based methods currently in wide use,
and more heavyweight, model-intensive methods such as Latent Dirichlet
Allocation (LDA). For a particular topic of interest (e.g., mental health
disability, or chemical reactions), we regress a labeling of documents onto the
high-dimensional counts of all the other words and phrases in the documents.
The resulting small set of phrases found as predictive are then harvested as
the summary. Using a branch-and-bound approach, this method can be extended to
allow for phrases of arbitrary length, which allows for potentially rich
summarization. We discuss how focus on the purpose of the summaries can inform
choices of regularization parameters and model constraints. We evaluate this
tool by comparing computational time and summary statistics of the resulting
word lists to three other methods in the literature. We also present a new R
package, textreg. Overall, we argue that sparse methods have much to offer text
analysis, and is a branch of research that should be considered further in this
context.
| 2,016 | Computation and Language |
Semi-supervised Bootstrapping approach for Named Entity Recognition | The aim of Named Entity Recognition (NER) is to identify references of named
entities in unstructured documents, and to classify them into pre-defined
semantic categories. NER often aids from added background knowledge in the form
of gazetteers. However using such a collection does not deal with name variants
and cannot resolve ambiguities associated in identifying the entities in
context and associating them with predefined categories. We present a
semi-supervised NER approach that starts with identifying named entities with a
small set of training data. Using the identified named entities, the word and
the context features are used to define the pattern. This pattern of each named
entity category is used as a seed pattern to identify the named entities in the
test set. Pattern scoring and tuple value score enables the generation of the
new patterns to identify the named entity categories. We have evaluated the
proposed system for English language with the dataset of tagged (IEER) and
untagged (CoNLL 2003) named entity corpus and for Tamil language with the
documents from the FIRE corpus and yield an average f-measure of 75% for both
the languages.
| 2,015 | Computation and Language |
Evaluating Prerequisite Qualities for Learning End-to-End Dialog Systems | A long-term goal of machine learning is to build intelligent conversational
agents. One recent popular approach is to train end-to-end models on a large
amount of real dialog transcripts between humans (Sordoni et al., 2015; Vinyals
& Le, 2015; Shang et al., 2015). However, this approach leaves many questions
unanswered as an understanding of the precise successes and shortcomings of
each model is hard to assess. A contrasting recent proposal are the bAbI tasks
(Weston et al., 2015b) which are synthetic data that measure the ability of
learning machines at various reasoning tasks over toy language. Unfortunately,
those tests are very small and hence may encourage methods that do not scale.
In this work, we propose a suite of new tasks of a much larger scale that
attempt to bridge the gap between the two regimes. Choosing the domain of
movies, we provide tasks that test the ability of models to answer factual
questions (utilizing OMDB), provide personalization (utilizing MovieLens),
carry short conversations about the two, and finally to perform on natural
dialogs from Reddit. We provide a dataset covering 75k movie entities and with
3.5M training examples. We present results of various models on these tasks,
and evaluate their performance.
| 2,016 | Computation and Language |
On the Linear Algebraic Structure of Distributed Word Representations | In this work, we leverage the linear algebraic structure of distributed word
representations to automatically extend knowledge bases and allow a machine to
learn new facts about the world. Our goal is to extract structured facts from
corpora in a simpler manner, without applying classifiers or patterns, and
using only the co-occurrence statistics of words. We demonstrate that the
linear algebraic structure of word embeddings can be used to reduce data
requirements for methods of learning facts. In particular, we demonstrate that
words belonging to a common category, or pairs of words satisfying a certain
relation, form a low-rank subspace in the projected space. We compute a basis
for this low-rank subspace using singular value decomposition (SVD), then use
this basis to discover new facts and to fit vectors for less frequent words
which we do not yet have vectors for.
| 2,015 | Computation and Language |
Non-Sentential Utterances in Dialogue: Experiments in Classification and
Interpretation | Non-sentential utterances (NSUs) are utterances that lack a complete
sentential form but whose meaning can be inferred from the dialogue context,
such as "OK", "where?", "probably at his apartment". The interpretation of
non-sentential utterances is an important problem in computational linguistics
since they constitute a frequent phenomena in dialogue and they are
intrinsically context-dependent. The interpretation of NSUs is the task of
retrieving their full semantic content from their form and the dialogue
context. The first half of this thesis is devoted to the NSU classification
task. Our work builds upon Fern\'andez et al. (2007) which present a series of
machine-learning experiments on the classification of NSUs. We extended their
approach with a combination of new features and semi-supervised learning
techniques. The empirical results presented in this thesis show a modest but
significant improvement over the state-of-the-art classification performance.
The consecutive, yet independent, problem is how to infer an appropriate
semantic representation of such NSUs on the basis of the dialogue context.
Fern\'andez (2006) formalizes this task in terms of "resolution rules" built on
top of the Type Theory with Records (TTR). Our work is focused on the
reimplementation of the resolution rules from Fern\'andez (2006) with a
probabilistic account of the dialogue state. The probabilistic rules formalism
Lison (2014) is particularly suited for this task because, similarly to the
framework developed by Ginzburg (2012) and Fern\'andez (2006), it involves the
specification of update rules on the variables of the dialogue state to capture
the dynamics of the conversation. However, the probabilistic rules can also
encode probabilistic knowledge, thereby providing a principled account of
ambiguities in the NSU resolution process.
| 2,015 | Computation and Language |
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