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elkiss et al carried out a largescale study and confirmed that citation summaries contain extra information that does not appear in paper abstracts . | elkiss et al , perform a large-scale study on citations in the free pubmed central and show that they contain information that may not be present in abstracts . |
dependency parsing is the task of predicting the most probable dependency structure for a given sentence . | dependency parsing is the task of building dependency links between words in a sentence , which has recently gained a wide interest in the natural language processing community . |
spelling variants can then be used to mitigate the problems caused by spelling variation that were described above . | alternatively , specialized tools can be developed that directly use the knowledge about spelling variation . |
arguably the most influential approach to the topic modeling domain is latent dirichlet allocation . | nowadays a very popular topic model is latent dirichlet allocation , a generative bayesian hierarchical model . |
sources of information are represented by kernel functions . | each source of information is represented by a specific kernel function . |
cui et al developed an information theoretic measure based on dependency trees . | cui et al developed a dependency-tree based information discrepancy measure . |
the 5-gram kneser-ney smoothed language models were trained by srilm , with kenlm used at runtime . | the language model pis implemented as an n-gram model using the srilm-toolkit with kneser-ney smoothing . |
and unseen predicates , we study the performance of a state-of-the-art srl system trained on either codification of roles and some specific settings , i . e . including / excluding verb-specific information . | by testing a state–of–the–art srl system with the two alternative role annotations , we show that the propbank role set is more robust to the lack of verb–specific semantic information and generalizes better to infrequent and unseen predicates . |
topic models such as lda and psla and their extensions have been popularly used to find topics in text documents . | topic models , such as plsa and lda , have shown great success in discovering latent topics in text collections . |
both the transfer and transducer systems were trained and evaluated on english-to-mandarin chinese translation of transcribed utterances from the atis corpus . | the head transducer model was trained and evaluated on english-to-mandarin chinese translation of transcribed utterances from the atis corpus . |
to address this problem , long short-term memory network was proposed in where the architecture of a standard rnn was modified to avoid vanishing or exploding gradients . | to tackle this problem , hochreiter and schmidhuber proposed long short term memory , which uses a cell with input , forget and output gates to prevent the vanishing gradient problem . |
the skip-gram model aims to find word representations that are useful for predicting the surrounding words in a sentence or document . | the skip-gram model implemented by word2vec learns vectors by predicting context words from targets . |
alignment types are shown with the ? symbol . | the incorrectly predicted tags are shown with the ? symbol . |
the weights of the log-linear interpolation model were optimized via minimum error rate training on the ted development set , using 200 best translations at each tuning iteration . | the weights of the different feature functions were optimised by means of minimum error rate training on the 2008 test set . |
relation extraction is the task of predicting semantic relations over entities expressed in structured or semi-structured text . | relation extraction ( re ) is the task of extracting instances of semantic relations between entities in unstructured data such as natural language text . |
jiang et al proposes a cascaded linear model for joint chinese word segmentation and pos tagging . | jiang et al used a character-based model using perceptron for pos tagging and a log-linear model for re-ranking . |
semantic role labeling ( srl ) is a form of shallow semantic parsing whose goal is to discover the predicate-argument structure of each predicate in a given input sentence . | semantic role labeling ( srl ) consists of finding the arguments of a predicate and labeling them with semantic roles ( cite-p-9-1-5 , cite-p-9-3-0 ) . |
while l-related candidate lexicalisation phrases are phrases containing synonyms or derivationally related . | in contrast , extensionally-related candidate lexicalisations are phrases containing named entities which are in its extension . |
for our classifiers , we used the weka implementation of na茂ve bayes and the svmlight implementation of the svm . | we used the weka implementation of na茂ve bayes for this baseline nb system . |
in particular , we use the liblinear svm 1va classifier . | we use liblinear 9 to solve the lr and svm classification problems . |
we used the srilm toolkit to create 5-gram language models with interpolated modified kneser-ney discounting . | we first trained a trigram bnlm as the baseline with interpolated kneser-ney smoothing , using srilm toolkit . |
the log-linear model features weights are tuned using the newswire part of nist mt06 as the tuning dataset and bleu as the objective function . | the smt systems are tuned on the dev development set with minimum error rate training using bleu accuracy measure as the optimization criterion . |
a 3-gram language model was trained from the target side of the training data for chinese and arabic , using the srilm toolkit . | srilm toolkit was used to create up to 5-gram language models using the mentioned resources . |
by including predictions of other models as features , we achieve aer of 3 . 8 . | by also including predictions of another model , we drive aer down to 3.8 . |
to the ad classification task , and our cnn-lstm model achieves a new benchmark accuracy . | we achieve a new independent benchmark accuracy for the ad classification task . |
distributional semantic models represent lexical meaning in vector spaces by encoding corpora derived word co-occurrences in vectors . | distributional semantic models produce vector representations which capture latent meanings hidden in association of words in documents . |
semantic parsing is the problem of mapping natural language strings into meaning representations . | semantic parsing is the problem of deriving a structured meaning representation from a natural language utterance . |
to solve this problem , hochreiter and schmidhuber introduced the long short-term memory rnn . | lstms were introduced by hochreiter and schmidhuber in order to mitigate the vanishing gradient problem . |
feng et al proposed accessor variety to measure the likelihood a substring is a chinese word . | feng et al proposed accessor variety to measure how likely a character substring is a chinese word . |
as an interesting byproduct , the earth mover ¡¯ s distance provides a distance measure that may quantify a facet of language difference . | in addition , we reveal an interesting finding that the earth mover¡¯s distance shows potential as a measure of language difference . |
for example , suendermann-oeft et al acquired 500,000 dialogues with over 2 million utterances , observing that statistical systems outperform rule-based ones as the amount of data increases . | for example , suendermann et al acquired 500,000 dialogues with over 2 million utterances , observin that statistical systems outperform rule-based ones as the amount of data increases . |
automatic summarisation is the task of reducing a document to its main points . | automatic summarisation is a popular approach to reduce a document to its main arguments . |
we used 5-gram models , estimated using the sri language modeling toolkit with modified kneser-ney smoothing . | we estimated 5-gram language models using the sri toolkit with modified kneser-ney smoothing . |
we used the stanford factored parser to retrieve both the stanford dependencies and the phrase structure parse . | we used the stanford factored parser to parse sentences into constituency grammar tree representations . |
transition-based methods have become a popular approach in multilingual dependency parsing because of their speed and performance . | such approaches , for example , transition-based and graph-based models have attracted the most attention in dependency parsing in recent works . |
in this work , we present a new method to do semantic abstractive summarization . | in this work , we propose an alternative method to use amrs for abstractive summarization . |
dinu and lapata introduced a probabilistic model for computing word representations in context . | dinu and lapata propose a probabilistic framework for representing word meaning and measuring similarity of words in context . |
a lattice is a connected directed acyclic graph in which each edge is labeled with a term hypothesis and a likelihood value ( cite-p-19-3-5 ) ; each path through a lattice gives a hypothesis of the sequence of terms spoken in the utterance . | a lattice is a directed acyclic graph that is used to compactly represent the search space for a speech recognition system . |
second , we propose a novel abstractive summarization technique based on an optimization framework that generates section-specific summaries for wikipedia . | second , we propose a novel abstractive summarization ( cite-p-10-1-6 ) technique to summarize content from multiple snippets of relevant information . |
zelenko et al and culotta and sorensen proposed kernels for dependency trees inspired by string kernels . | zelenko et al developed a kernel over parse trees for relation extraction . |
coreference resolution is the task of determining which mentions in a text are used to refer to the same real-world entity . | coreference resolution is a well known clustering task in natural language processing . |
word segmentation is a fundamental task for chinese language processing . | therefore , word segmentation is a preliminary and important preprocess for chinese language processing . |
and then we extract subtrees from dependency parsing trees in the auto-parsed data . | then we extract subtrees from dependency parse trees in the auto-parsed data . |
clark and curran describes how a packed chart can be used to efficiently represent the derivation space , and also efficient algorithms for finding the most probable derivation . | and clark and curran describe how a packed chart can be used to efficiently represent the derivation space , and also efficient algorithms for finding the most probable derivation . |
we also need a restriction on the entity-tuple embedding space . | the presented approach requires a restriction on the entity-tuple embedding space . |
semantic parsing is the task of mapping natural language sentences to a formal representation of meaning . | semantic parsing is the task of mapping a natural language ( nl ) sentence into a complete , formal meaning representation ( mr ) which a computer program can execute to perform some task , like answering database queries or controlling a robot . |
as a further test , we ran the stanford parser on the queries to generate syntactic parse trees . | we obtained both phrase structures and dependency relations for every sentence using the stanford parser . |
the mre is the shortest possible summary of a story ; it is what we would say about the story if we could only say one thing . | the mre is the point of the story – the most unusual event that has the greatest emotional impact on the narrator and the audience . |
weber et al used three-dimensional tensor-based networks to construct the event representations . | weber et al proposed a tensor-based composition model to construct event embeddings with agents and patients . |
method is effective , and is a key technology enabling smooth conversation with a dialogue translation system . | therefore , we can say that our method is effective for smooth conversation with a dialogue translation system . |
heilman et al studied the impact of grammar-based features combined with language modeling approach for readability assessment of first and second language texts . | heilman et al combined unigram models with grammatical features and trained machine learning models for readability assessment . |
nlg is a critical component in a dialogue system , where its goal is to generate the natural language given the semantics provided by the dialogue manager . | informally , nlg is the production of a natural language text from computer-internal representation of information , where nlg can be seen as a complex -- potentially cascaded -- decision making process . |
although wordnet is a fine resources , we believe that ignoring other thesauri is a serious oversight . | wordnet is a byproduct of such an analysis . |
the method of tsvetkov et al used both concreteness features and hand-coded domain information for words . | tsvetkov et al presented a language-independent approach to metaphor identification . |
we evaluate the translation quality using the case-insensitive bleu-4 metric . | to evaluate segment translation quality , we use corpus level bleu . |
in this paper , we study the use of more expressive loss functions in the structured prediction framework for cr , although . | in this paper , we trade off exact computation for enabling the use of more complex loss functions for coreference resolution ( cr ) . |
it is shown that the structure of semantic concepts helps decide domain-specific slots and further improves the slu performance . | also , dependency relations successfully differentiate the generic concepts from the domain-specific concepts , so that the slu model is able to predict more coherent set of semantic slots . |
although such approaches perform reasonably well , features are often derived from language-specific resources . | a more promising approach is to automatically learn effective features from data , without relying on language-specific resources . |
erkan and radev introduced a stochastic graph-based method , lexrank , for computing the relative importance of textual units for multi-document summarization . | erkan and radev proposed lexpagerank to compute the sentence saliency based on the concept of eigenvector centrality . |
the srilm toolkit was used to build the trigram mkn smoothed language model . | we used a 5-gram language model with modified kneser-ney smoothing , built with the srilm toolkit . |
the model weights were trained using the minimum error rate training algorithm . | the feature weights are tuned to optimize bleu using the minimum error rate training algorithm . |
such features have been useful in a variety of english nlp models , including chunking , named entity recognition , and spoken language understanding . | importantly , word embeddings have been effectively used for several nlp tasks , such as named entity recognition , machine translation and part-of-speech tagging . |
however , s-lstm models hierarchical encoding of sentence structure as a recurrent state . | empirically , s-lstm can give effective sentence encoding after 3 ¨c 6 recurrent steps . |
the weights for these features are optimized using mert . | all the weights of those features are tuned by using minimal error rate training . |
hamilton et al propose the use of cosine similarities of words in different contexts to detect changes . | similarly , hamilton et al defined a methodology to quantify semantic change using four languages . |
as a sequence labeler we use conditional random fields . | for parameter training we use conditional random fields as described in . |
we apply several unsupervised and supervised techniques of sentiment composition to determine their efficacy . | finally , we apply several unsupervised and supervised techniques of sentiment composition to determine their efficacy on this dataset . |
stance detection is the task of estimating whether the attitude expressed in a text towards a given topic is ‘ in favour ’ , ‘ against ’ , or ‘ neutral ’ . | stance detection is the task of automatically determining from text whether the author of the text is in favor of , against , or neutral towards a proposition or target . |
as a sequence labeler we use conditional random fields . | we define a conditional random field for this task . |
we give a brief ( and non-exhaustive ) overview of prior work on gender bias . | we present an empirical study of gender bias in coreference resolution systems . |
sentiment classification is a well-studied and active research area ( cite-p-20-1-11 ) . | sentiment classification is the fundamental task of sentiment analysis ( cite-p-15-3-11 ) , where we are to classify the sentiment of a given text . |
cite-p-27-1-12 proposed a method calculating word candidates with their unigram frequencies . | cite-p-27-1-11 proposed a stochastic word segmenter based on a word -gram model to solve the word segmentation problem . |
lsa has remained as a popular approach for asag and been applied in many variations . | lsa has remained a popular approach for asag and been applied in many variations . |
we used the maximum entropy approach 5 as a machine learner for this task . | we utilize a maximum entropy model to design the basic classifier used in active learning for wsd . |
semantic role labeling ( srl ) is the task of labeling the predicate-argument structures of sentences with semantic frames and their roles ( cite-p-18-1-2 , cite-p-18-1-19 ) . | semantic role labeling ( srl ) is the task of automatically labeling predicates and arguments in a sentence with shallow semantic labels . |
part-of-speech ( pos ) tagging is a job to assign a proper pos tag to each linguistic unit such as word for a given sentence . | part-of-speech ( pos ) tagging is a fundamental natural-language-processing problem , and pos tags are used as input to many important applications . |
for all the methods in this section , we use the same corpus , the icwsm spinn3r 2009 dataset , which has been used successfully in earlier work . | for both attributes addressed in this paper , we use the same corpus , the 2009 icwsm spinn3r dataset , a publicly-available blog corpus which we also used in our earlier work on lexical formality . |
we use the pre-trained word2vec embeddings provided by mikolov et al as model input . | as embedding vectors , we used the publicly available representations obtained from the word2vec cbow model . |
we used the moses decoder , with default settings , to obtain the translations . | we adapted the moses phrase-based decoder to translate word lattices . |
conjuncts tend to be similar and ( b ) that replacing the coordination phrase with a conjunct results in a coherent sentence . | replacing a conjunct with the whole coordination phrase usually produce a coherent sentence ( huddleston et al. , 2002 ) . |
we use the pre-trained glove vectors to initialize word embeddings . | in our experiments , the pre-trained word embeddings for english are 100-dimensional glove vectors . |
socher et al , 2012 ) presented a recursive neural network for relation classification to learn vectors in the syntactic tree path connecting two nominals to determine their semantic relationship . | socher et al present a novel recursive neural network for relation classification that learns vectors in the syntactic tree path that connects two nominals to determine their semantic relationship . |
the abstract meaning representation is a semantic meaning representation language that is purposefully syntax-agnostic . | the abstract meaning representation is a readable and compact framework for broad-coverage semantic annotation of english sentences . |
dong et al use three columns of cnns to represent questions respectively when dealing with different answer aspects . | dong et al employs three fixed cnns to represent questions , while ours is able to express the focus of each unique answer aspect to the words in the question . |
automatic word alignment is a vital component of nearly all current statistical translation pipelines . | automatic word alignment is a key step in training statistical machine translation systems . |
zhang and kim developed a system for automated learning of morphological word formation rules . | kim developed a system for automated learning of morphological word function rules . |
we measure the translation quality using a single reference bleu . | we evaluate the translation quality using the case-insensitive bleu-4 metric . |
we focus much more on the analysis of concept drift . | we demonstrate that concept drift is an important consideration . |
with the shared task , we aimed to make a first step towards taking srl beyond the domain of individual sentences . | in that sense the task represents a first step towards taking srl beyond the sentence level . |
t ype sql + tc gains roughly 9 % improvement compared to the content-insensitive model , and outperforms the previous content-sensitive model . | t ype sql gets 82.6 % accuracy , a 17.5 % absolute improvement compared to the previous content-sensitive model . |
in section 3 and 4 , we formally define the task . | in section 3 and 4 , we formally define the task and present our method . |
such a model can be used for topic identification of unseen calls . | such a model can be used for identification of topics of unseen calls . |
in order to increase the number of training instances , we tried to use the disambiguated wordnet glosses from xwn project . | in addition we used disambiguated wordnet glosses from xwn to measure the improvement made by adding additional training examples . |
choosing an appropriate entity and its mention has a big influence on the coherence of a text , as studied in centering theory . | according to the centering theory , the coherence of text is to a large extent maintained by entities and the relations between them . |
the algorithm is similar to those for context free parsing such as chart parsing and the cky algorithm . | the algorithm is similar to those for context-free parsing such as chart parsing and the cky algorithm . |
in this work , we use bleu-4 score as the evaluation metric , which measures the overlap between the generated question and the referenced question . | we evaluate the performance of k2q rnn with other baselines to compare the k2q approaches , we use bleu score between the generated question and the reference question . |
metonymy is typically defined as a figure of speech in which a speaker uses one entity to refer to another that is related to it ( cite-p-10-1-3 ) . | metonymy is a pervasive phenomenon in language and the interpretation of metonymic expressions can impact tasks from semantic parsing ( cite-p-13-1-10 ) to question answering ( cite-p-13-1-4 ) . |
recent years have witnessed the success of various statistical machine translation models using different levels of linguistic knowledgephrase , hiero , and syntax-based . | recent years have witnessed burgeoning development of statistical machine translation research , notably phrase-based and syntax-based approaches . |
and compare our method with a monolingual syntax-based method . | we will refer to such systems as monolingual syntax-based systems . |
distributed representations for words and sentences have been shown to significantly boost the performance of a nlp system . | it has been empirically shown that word embeddings could capture semantic and syntactic similarities between words . |
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