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Throughout this paper we compare with several packages: SRILM 1.5.12 (Stolcke, 2002) is a popular toolkit based on tries used in several decoders.
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Minimal perfect hashing is used to find the index at which a quantized probability and possibly backoff are stored.
Instance-weighting approach improved over a wide range of baselines, giving gains of over 2 BLEU points over the best non-adapted baseline.
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Somewhat surprisingly, there do not appear to be large systematic differences between linear and MAP combinations.
Throughout this paper we compare with several packages: SRILM 1.5.12 (Stolcke, 2002) is a popular toolkit based on tries used in several decoders.
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The cost of storing these averages, in bits, is Because there are comparatively few unigrams, we elected to store them byte-aligned and unquantized, making every query faster.
This architecture provides a uniform framework in which it is easy to incorporate not only listed dictionary entries but also morphological derivatives, and models for personal names and foreign names in transliteration.
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As described in Sproat (1995), the Chinese segmenter presented here fits directly into the context of a broader finite-state model of text analysis for speech synthesis.
Other kinds of productive word classes, such as company names, abbreviations,and place names can easily be handled given appropriate models.
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4.4 Chinese Personal Names.
In this paper the authors present a stochastic finite-state model for segmenting Chinese text into words.
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We asked six native speakers-three from Taiwan (TlT3), and three from the Mainland (M1M3)-to segment the corpus.
Due to many similarly performing systems, the author was not able to draw strong conclusions on the question of correlation of manual and automatic evaluation metrics.
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We carried out an extensive manual and automatic evaluation of machine translation performance on European language pairs.
Bean and Riloff also used bootstrapping to extend their semantic compatibility model, proposed using caseframe network for anaphora resolution, information extraction patterns to identify contextual clues for determining compatibility between NPs.
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Table 2 shows BABAR’s performance.
The code is open source, has minimal dependencies, and offers both C++ and Java interfaces for integration.
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RandLM and SRILM also remove context that will not extend, but SRILM performs a second lookup in its trie whereas our approach has minimal additional cost.
However, these approaches are ill-equipped to directly represent type-based constraints such as sparsity.
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As is standard, we use a fixed constant K for the number of tagging states.
They have made use of local and global features to deal with the instances of same token in a document.
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We often encounter sentences that are highly ambiguous in themselves, without some prior knowledge of the entities concerned.
There are clustering approaches that assign a single POS tag to each word type.
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In this work, we take a more direct approach and treat a word type and its allowed POS tags as a primary element of the model.
The code is open source, has minimal dependencies, and offers both C++ and Java interfaces for integration.
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Interpolation search formalizes the notion that one opens a dictionary near the end to find the word “zebra.” Initially, the algorithm knows the array begins at b +— 0 and ends at e +— |A|−1.
NER is useful in many NLP applications such as information extraction, question answering, etc .Chieu and Ng have shown that the maximum entropy framework is able to use global information directly from various sources.
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If is a number string (such as one, two, etc), then the feature NumberString is set to 1.
It is well-known that English constituency parsing models do not generalize to other languages and treebanks.
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02 99.
This paper conducted research in the area of automatic paraphrase discovery.
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As you can see in the figure, the accuracy for the domain is quite high except for the “agree” set, which contains various expressions representing different relationships for an IE application.
Through parser combination we have reduced the precision error rate by 30% and the recall error rate by 6% compared to the best previously published result.
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This is not an oversight.
The bias of automatic methods in favour of statistical systems seems to be less pronounced on out-of-domain test data.
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This data set of manual judgements should provide a fruitful resource for research on better automatic scoring methods.
they showed that the average agreement among the human judges is .76, and the average agreement between ST(system) and the humans is .75, or about 99% of the interhuman agreement.
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For example, given a sequence F1G1G2, where F1 is a legal single-hanzi family name, and Plural Nouns X g 0 g "' X X 0 T!i c"'.
In order to handle the necessary word reordering as an optimization problem within the dynamic programming approach, they describe a solution to the traveling salesman problem (TSP) which is based on dynamic programming.
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Subsets of partial hypotheses with coverage sets C of increasing cardinality c are processed.
This corpus has several advantages: it is annotated at different levels.
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The implementation is In a similar effort, (G¨otze 2003) developed a proposal for the theory-neutral annotation of information structure (IS) — a notoriously difficult area with plenty of conflicting and overlapping terminological conceptions.
The fact that projective dependency parsers can never exactly reproduce the analyses found in non-projective tree banks is often neglected because of the relative scarcity of problematic constructions.
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However, while Dienes and Dubey recognize empty categories in a pre-processing step and only let the parser find their antecedents, we use the parser both to detect dislocated dependents and to predict either the type or the location of their syntactic head (or both) and use post-processing only to transform the graph in accordance with the parser’s analysis.
Explanations for this phenomenon are relative informativeness of lexicalization, insensitivity to morphology and the effect of variable word order and these factors lead to syntactic disambiguation.
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For a corpus C, let M be the set of tuples ∗n, l), where n is an n-gram with bracketing label l. If any n appears 6 Generative parsing performance is known to deteriorate with sentence length.
The use of global features has shown excellent result in the performance on MUC-6 and MUC-7 test data.
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If is seen infrequently during training (less than a small count), then will not be selected as a feature and all features in this group are set to 0.
The second algorithm builds on a boosting algorithm called AdaBoost.
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Note, however, that there might be situations in which Zco in fact increases.
Their results show that their high performance NER use less training data than other systems.
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Context from the whole document can be important in classifying a named entity.
Nevertheless, only a part of this corpus (10 texts), which the authors name "core corpus", is annotated with all this information.
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Still, for both human and automatic rhetorical analysis, connectives are the most important source of surface information.
This paper presents a maximum entropy-based named entity recognizer (NER).
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3.1 Maximum Entropy.
The texts were annotated with the RSTtool.
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The paper is organized as follows: Section 2 explains the different layers of annotation that have been produced or are being produced.
Using a treebank grammar, the data-driven lexicon, and a linguistically motivated unknown-tokens handling technique their model outperformed previous pipelined, integrated or factorized systems for Hebrew morphological and syntactic processing, yielding an error reduction of 12% over the best published results so far.
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Here, all tokeninternal collocations of tags unseen in our training data are pruned away.
Vijay-Shankar et all considered the structural descriptions produced by various grammatical formalisms in terms of the complexity of the paths and the relationship between paths in the sets of structural descriptions that each system can generate.
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These operations, as we see below, are restricted to be size preserving (as in the case of concatenation in CFG) which implies that they will be linear and non-erasing.
They showed that it was useful to abstract away from the details of the formalism, and examine the nature of their derivation process as reflected by properties their trees, find that several of the formalisms considered can be seen as being closely related since they have derivation tree sets with the same structure as those produced by Context-Free Grammars.
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Nodes are annotated by the name of the composition operation used at that step in the derivation.
They extended previous work on discriminative weighting by using a finer granularity, focusing on the properties of instances rather than corpus components, and used simpler training procedure.
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We carried out translation experiments in two different settings.
This paper offers a broad insight into of Arabic constituency parsing by analyzing the interplay of linguistic phenomena, annotation choices, and model design.
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(2003), which gives 96.8% accuracy on the test set.
A beam search concept is applied as in speech recognition.
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The complexity of the algorithm is O(E3 J2 2J), where E is the size of the target language vocabulary.
While many systems had similar performance, the results offered interesting insights, especially, about the relative performance of statistical and rule-based systems.
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By taking the ratio of matching n-grams to the total number of n-grams in the system output, we obtain the precision pn for each n-gram order n. These values for n-gram precision are combined into a BLEU score: The formula for the BLEU metric also includes a brevity penalty for too short output, which is based on the total number of words in the system output c and in the reference r. BLEU is sensitive to tokenization.
In order to handle the necessary word reordering as an optimization problem within the dynamic programming approach, they describe a solution to the traveling salesman problem (TSP) which is based on dynamic programming.
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For the error counts, a range from 0:0 to 1:0 is used.
The approach assumes that the word reordering is restricted to a few positions in the source sentence.
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German city names are replaced by category markers.
The authors use linguistic and annotation insights to develop a manually annotated grammar and evaluate it and finally provide a realistic evaluation in which segmentation is performed in a pipeline jointly with parsing.
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5.2 Discussion.
Bean and Riloff also used bootstrapping to extend their semantic compatibility model, proposed using caseframe network for anaphora resolution, information extraction patterns to identify contextual clues for determining compatibility between NPs.
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BABAR uses unsupervised learning to acquire this knowledge from plain text without the need for annotated training data.
The fact that projective dependency parsers can never exactly reproduce the analyses found in non-projective tree banks is often neglected because of the relative scarcity of problematic constructions.
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In the third and final scheme, denoted Path, we keep the extra infor2Note that this is a baseline for the parsing experiment only (Experiment 2).
Explanations for this phenomenon are relative informativeness of lexicalization, insensitivity to morphology and the effect of variable word order and these factors lead to syntactic disambiguation.
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This paper is based on work supported in part by DARPA through IBM.
This topic has been getting more attention, driven by the needs of various NLP applications.
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Also, we don’t know how many such paraphrase sets are necessary to cover even some everyday things or events.
This paper talks about KenLM: Faster and Smaller Language Model Queries.
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While the paper mentioned a sorted variant, code was never released.
They incorporated instance-weighting into a mixture-model framework, and found that it yielded consistent improvements over a wide range of baselines.
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In this paper we study the problem of using a parallel corpus from a background domain (OUT) to improve performance on a target domain (IN) for which a smaller amount of parallel training material—though adequate for reasonable performance—is also available.
They have made use of local and global features to deal with the instances of same token in a document.
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Except our own and MENE + reference resolution, the results in Table 6 are all official MUC7 results.
It is well-known that English constituency parsing models do not generalize to other languages and treebanks.
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Arabic sentences of up to length 63 would need to be.
Their method did not assume any knowledge about the target language, making it applicable to a wide array of resource-poor languages.
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The first term in the objective function is the graph smoothness regularizer which encourages the distributions of similar vertices (large wij) to be similar.
It is probably the first analysis of Arabic parsing of this kind.
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Parent Head Modif er Dir # gold F1 Label # gold F1 NP NP TAG R 946 0.54 ADJP 1216 59.45 S S S R 708 0.57 SBAR 2918 69.81 NP NP ADJ P R 803 0.64 FRAG 254 72.87 NP NP N P R 2907 0.66 VP 5507 78.83 NP NP SBA R R 1035 0.67 S 6579 78.91 NP NP P P R 2713 0.67 PP 7516 80.93 VP TAG P P R 3230 0.80 NP 34025 84.95 NP NP TAG L 805 0.85 ADVP 1093 90.64 VP TAG SBA R R 772 0.86 WHN P 787 96.00 S VP N P L 961 0.87 (a) Major phrasal categories (b) Major POS categories (c) Ten lowest scoring (Collins, 2003)-style dependencies occurring more than 700 times Table 8: Per category performance of the Berkeley parser on sentence lengths ≤ 70 (dev set, gold segmentation).
A large number of rules are needed for coverage of the domain, suggesting that a fairly large number of labeled examples should be required to train a classifier.
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In the cotraining case, (Blum and Mitchell 98) argue that the task should be to induce functions Ii and f2 such that So Ii and 12 must (1) correctly classify the labeled examples, and (2) must agree with each other on the unlabeled examples.
Their results show that their high performance NER use less training data than other systems.
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Mikheev et al.
The resulting model is compact, efficiently learnable and linguistically expressive.
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Tag set As is standard, for all experiments, we set the number of latent model tag states to the size of the annotated tag set.
It is annotated with several data: morphology, syntax, rhetorical structure, connectors, correference and informative structure.
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We will briefly discuss this point in Section 3.1.
It also incorporates the Good-Turing methodin estimating the likelihoods of previously unseen constructions, including morphological derivatives and personal names.
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Both of these analyses are shown in Figure 4; fortunately, the correct analysis is also the one with the lowest cost, so it is this analysis that is chosen.
Instance-weighting approach improved over a wide range of baselines, giving gains of over 2 BLEU points over the best non-adapted baseline.
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Sentence pairs are the natural instances for SMT, but sentences often contain a mix of domain-specific and general language.
In this paper, Ben and Riloff present a coreference resolver called BABAR that focuses on the use of contextual-role knowledge for coreference resolution.
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For example, management succession systems must distinguish between a person who is fired and a person who is hired.
This assumption, however, is not inherent to type-based tagging models.
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token-level HMM to reflect lexicon sparsity.
They employed a PCFG-based generative framework to make both syntactic and morphological disambiguation decisions which were not only theoretically clean and linguistically justified but also probabilistically appropriate and empirically sound.
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To control for the effect of the HSPELL-based pruning, we also experimented with a morphological analyzer that does not perform this pruning.
The code is open source, has minimal dependencies, and offers both C++ and Java interfaces for integration.
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Though we are not able to calculate their memory usage on our model, results reported in their paper suggest lower memory consumption than TRIE on large-scale models, at the expense of CPU time.
they evaluate various specific aspects of the segmentation, as well as the overall segmentation performance.
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Twentieth-century linguistic work on Chinese (Chao 1968; Li and Thompson 1981; Tang 1988,1989, inter alia) has revealed the incorrectness of this traditional view.
Across eight European languages, their approach results in an average absolute improvement of 10.4% over a state-of-the-art baseline, and 16.7% over vanilla hidden Markov models induced with the Expectation Maximization algorithm.
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1).
Two general approaches are presented and two combination techniques are described for each approach.
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When this metric is less than 0.5, we expect to incur more errors' than we will remove by adding those constituents to the parse.
The authors show that PATB is similar to other tree-banks but that annotation consistency remains low.
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83 77.
We chose one of four labels for each example: location, person, organization, or noise where the noise category was used for items that were outside the three categories.
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(Blum and Mitchell 98) give an example that illustrates just how powerful the second constraint can be.
they showed that the average agreement among the human judges is .76, and the average agreement between ST(system) and the humans is .75, or about 99% of the interhuman agreement.
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We have provided methods for handling certain classes of unknown words, and models for other classes could be provided, as we have noted.
Foster et all describe a new approach to SMT adaptation that weights out-of-domain phrase pairs according to their relevance to the target domain, determined by both how similar to it they appear to be, and whether they belong to general language or not.
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For developers of Statistical Machine Translation (SMT) systems, an additional complication is the heterogeneous nature of SMT components (word-alignment model, language model, translation model, etc.
Other kinds of productive word classes, such as company names, abbreviations,and place names can easily be handled given appropriate models.
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To date we have not done a separate evaluation of foreign-name recognition.
The authors use linguistic and annotation insights to develop a manually annotated grammar and evaluate it and finally provide a realistic evaluation in which segmentation is performed in a pipeline jointly with parsing.
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43 80.
This paper talks about KenLM: Faster and Smaller Language Model Queries.
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If there are too many distinct states, the decoder prunes low-scoring partial hypotheses, possibly leading to a search error.
they evaluate various specific aspects of the segmentation, as well as the overall segmentation performance.
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However, as we have noted, nothing inherent in the approach precludes incorporating higher-order constraints, provided they can be effectively modeled within a finite-state framework.
Their results suggested that it was possible to learn accurate POS taggers for languages which did not have any annotated data, but have translations into a resource-rich language.
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We adopted this state-of-the-art model because it makes it easy to experiment with various ways of incorporating our novel constraint feature into the log-linear emission model.
For the perplexity and translation tasks, we used SRILM to build a 5-gram English language model on 834 million tokens from Europarl v6 (Koehn, 2005) and the 2011 Workshop on Machine Translation News Crawl corpus with duplicate lines removed.
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The structure uses linear probing hash tables and is designed for speed.
Human judges also pointed out difficulties with the evaluation of long sentences.
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Human judges also pointed out difficulties with the evaluation of long sentences.
They employed a PCFG-based generative framework to make both syntactic and morphological disambiguation decisions which were not only theoretically clean and linguistically justified but also probabilistically appropriate and empirically sound.
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The Input The set of analyses for a token is thus represented as a lattice in which every arc corresponds to a specific lexeme l, as shown in Figure 1.
They focused on phrases which two Named Entities, and proceed in two stages.
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It is natural that the larger the data in the domain, the more keywords are found.
Due to many similarly performing systems, they are not able to draw strong conclusions on the question of correlation of manual and automatic evaluation metrics.
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Hence, the different averages of manual scores for the different language pairs reflect the behaviour of the judges, not the quality of the systems on different language pairs.
It is annotated with several data: morphology, syntax, rhetorical structure, connectors, correference and informative structure.
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The corpus has been annotated with six different types of information, which are characterized in the following subsections.
The manual evaluation of scoring translation on a graded scale from 1–5 seemed to be very hard to perform.
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For statistics on this test set, refer to Figure 1.
They proposed an unsupervised method to discover paraphrases from a large untagged corpus.
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We proposed an unsupervised method to discover paraphrases from a large untagged corpus.
In this paper the authors present a stochastic finite-state model for segmenting Chinese text into words.
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In this paper we have argued that Chinese word segmentation can be modeled ef­ fectively using weighted finite-state transducers.
In this work, they take a more direct approach and treat a word type and its allowed POS tags as a primary element of the model.
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Across all languages, +PRIOR consistently outperforms 1TW, reducing error on average by 9.1% and 5.9% on best and median settings respectively.
They plan on extending instance-weighting to other standard SMT components and capture the degree of generality of phrase pairs.
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First, we learn weights on individual phrase pairs rather than sentences.
Evaluation was done automatically using the BLEU score and manually on fluency and adequacy.
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Annotators suggested that long sentences are almost impossible to judge.
Combining multiple highly-accurate independent parsers yields promising results.
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In the cases where isolated constituent precision is larger than 0.5 the affected portion of the hypotheses is negligible.
they evaluate various specific aspects of the segmentation, as well as the overall segmentation performance.
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While Gan's system incorporates fairly sophisticated models of various linguistic information, it has the drawback that it has only been tested with a very small lexicon (a few hundred words) and on a very small test set (thirty sentences); there is therefore serious concern as to whether the methods that he discusses are scalable.
This topic has been getting more attention, driven by the needs of various NLP applications.
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In this domain the major scenarios involve the things they agreed on, rather than the mere fact that they agreed.
They proposed an unsupervised method to discover paraphrases from a large untagged corpus.
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They contain about 200M words (25M, 110M, 40M and 19M words, respectively).
This paper conducted research in the area of automatic paraphrase discovery.
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The accuracies for link were 73% and 86% on two evaluated domains.
Other kinds of productive word classes, such as company names, abbreviations,and place names can easily be handled given appropriate models.
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However, there is again local grammatical information that should favor the split in the case of (1a): both .ma3 'horse' and .ma3 lu4 are nouns, but only .ma3 is consistent with the classifier pil, the classifier for horses.21 By a similar argument, the preference for not splitting , lm could be strengthened in (lb) by the observation that the classifier 1'1* tiao2 is consistent with long or winding objects like , lm ma3lu4 'road' but not with,ma3 'horse.'
they evaluate various specific aspects of the segmentation, as well as the overall segmentation performance.
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However, the characterization given in the main body of the text is correct sufficiently often to be useful.
The three parsers were trained and tuned by their creators on various sections of the WSJ portion of the Penn Treebank.
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The estimation of the probabilities in the model is carried out as shown in Equation 4.
Instance-weighting approach improved over a wide range of baselines, giving gains of over 2 BLEU points over the best non-adapted baseline.
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Section 2 describes our baseline techniques for SMT adaptation, and section 3 describes the instance-weighting approach.
The authors show that PATB is similar to other tree-banks but that annotation consistency remains low.
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To our knowledge, ours is the first analysis of this kind for Arabic parsing.
This assumption, however, is not inherent to type-based tagging models.
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However, in existing systems, this expansion come with a steep increase in model complexity.
This corpus has several advantages: it is annotated at different levels.
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This concerns on the one hand the basic question of retrieval, i.e. searching for information across the annotation layers (see 3.1).
This paper offers a broad insight into of Arabic constituency parsing by analyzing the interplay of linguistic phenomena, annotation choices, and model design.
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Each model was able to produce hypotheses for all input sentences.
Through parser combination we have reduced the precision error rate by 30% and the recall error rate by 6% compared to the best previously published result.
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The substructures that are unanimously hypothesized by the parsers should be preserved after combination, and the combination technique should not foolishly create substructures for which there is no supporting evidence.
The experimental tests are carried out on the Verbmobil task which is a limited-domain spoken-language task.
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The translation search is carried out with the category markers and the city names are resubstituted into the target sentence as a postprocessing step.
In this paper, the authors are of the opinion that the sequence models-based approaches usually treat token-level tag assignment as the primary latent variable.
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See Section 5.
In order to realize the full potential of dependency-based syntactic parsing, it is desirable to allow non-projective dependency structures.
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The dependency graph in Figure 1 satisfies all the defining conditions above, but it fails to satisfy the condition ofprojectivity (Kahane et al., 1998): The arc connecting the head jedna (one) to the dependent Z (out-of) spans the token je (is), which is not dominated by jedna.