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Their empirical results demonstrate that the type-based tagger rivals state-of-the-art tag-level taggers which employ more sophisticated learning mechanisms to exploit similar constraints.
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For instance, on Spanish, the absolute gap on median performance is 10%.
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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(b) POS tagging accuracy is lowest for maSdar verbal nouns (VBG,VN) and adjectives (e.g., JJ).
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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We are also grateful to Markus Dickinson, Ali Farghaly, Nizar Habash, Seth Kulick, David McCloskey, Claude Reichard, Ryan Roth, and Reut Tsarfaty for constructive discussions.
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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Equation 2 is an estimate of the conditional probability of the label given the feature, P(yjx).
This paper conducted research in the area of automatic paraphrase discovery.
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There have been other kinds of efforts to discover paraphrase automatically from corpora.
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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01 75.
The manual evaluation of scoring translation on a graded scale from 1–5 seemed to be very hard to perform.
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We therefore also normalized judgements on a per-sentence basis.
From a DP-based solution to the traveling salesman problem, they present a novel technique to restrict the possible word reordering between source and target language in order to achieve an eÆcient search algorithm.
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E.g. when 'Zahnarzttermin' is aligned to dentist's, the extended lexicon model might learn that 'Zahnarzttermin' actuallyhas to be aligned to both dentist's and ap pointment.
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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During coreference resolution, the caseframe network provides evidence that an anaphor and prior noun phrase might be coreferent.
The problem of coreference resolution has received considerable attention, including theoretical discourse models and supervised machine learning systems.
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This approach is similar to BABAR in that they both acquire knowledge from earlier resolutions.
Here we present two algorithms.
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Then, it can be verified that We can now derive the CoBoost algorithm as a means of minimizing Zco.
The manual evaluation of scoring translation on a graded scale from 1–5 seemed to be very hard to perform.
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One annotator suggested that this was the case for as much as 10% of our test sentences.
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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If two systems’ scores are close, this may simply be a random effect in the test data.
In this paper the authors present a stochastic finite-state model for segmenting Chinese text into words.
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Table 2 Similarity matrix for segmentation judgments.
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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These sequence models-based approaches commonly treat token-level tag assignment as the primary latent variable.
This paper presents a maximum entropy-based named entity recognizer (NER).
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Reference resolution involves finding words that co-refer to the same entity.
The manual evaluation of scoring translation on a graded scale from 1–5 seemed to be very hard to perform.
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Annotators suggested that long sentences are almost impossible to judge.
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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Given this similarity function, we define a nearest neighbor graph, where the edge weight for the n most similar vertices is set to the value of the similarity function and to 0 for all other vertices.
They have made use of local and global features to deal with the instances of same token in a document.
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Feature selection is implemented using a feature cutoff: features seen less than a small count during training will not be used.
The manual evaluation of scoring translation on a graded scale from 1–5 seems to be very hard to perform.
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This is less than the 694 judgements 2004 DARPA/NIST evaluation, or the 532 judgements in the 2005 DARPA/NIST evaluation.
The experimental tests are carried out on the Verbmobil task which is a limited-domain spoken-language task.
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Again, the monotone search performs worst.
They believe that global context is useful in most languages, as it is a natural tendency for authors to use abbreviations on entities already mentioned previously.
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of Tokens MENERGI 318 160,000 200 180,000 IdentiFinder – 650,000 – 790,000 MENE – – 350 321,000 Table 4: Training Data MUC7 test accuracy.2 For MUC6, the reduction in error due to global features is 27%, and for MUC7,14%.
These clusters are computed using an SVD variant without relying on transitional structure.
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This design leads to a significant reduction in the computational complexity of training and inference.
The features were weighted within a logistic model that gave an overall weight that was applied to the phrase pair and MAP-smoothed relative-frequency estimates which were combined linearly with relative-frequency estimates from an in-domain phrase table.
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In this paper we have proposed an approach for instance-weighting phrase pairs in an out-of-domain corpus in order to improve in-domain performance.
This corpus has several advantages: it is annotated at different levels.
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Due to the dramatic fiscal situation in Brandenburg she now surprisingly withdrew legislation drafted more than a year ago, and suggested to decide on it not before 2003.
Finally, several coreference systems have successfully incorporated anaphoricity determination modules.
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For example, if X and Y are coreferent, then both X and Y are considered to co-occur with the caseframe that extracts X as well as the caseframe that extracts Y. We will refer to the set of nouns that co-occur with a caseframe as the lexical expectations of the case- frame.
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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First we mark any node that dominates (at any level) a verb sider POS tags when pre-terminals are the only intervening nodes between the nucleus and its bracketing (e.g., unaries, base NPs).
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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The model described here thus demonstrates great potential for use in widespread applications.
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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In (1) the sequencema3lu4 cannot be resolved locally, but depends instead upon broader context; similarly in (2), the sequence :::tcai2neng2 cannot be resolved locally: 1.
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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I = 1X21 N and N is a "medium" sized number so that it is feasible to collect 0(N) unlabeled examples.
Each out-of-domain phrase pair was characterized by a set of simple features intended to reflect how useful it would be.
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It is not immediately obvious how to formulate an equivalent to equation (1) for an adapted TM, because there is no well-defined objective for learning TMs from parallel corpora.
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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Zone: MUC data contains SGML tags, and a document is divided into zones (e.g., headlines and text zones).
They proposed an unsupervised method to discover paraphrases from a large untagged corpus.
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Smith estimates Lotus will make profit this quarter…”.
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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Conversely, the lattice parser requires no linguistic resources and produces segmentations of comparable quality.
This paper talks about Exploiting Diversity in Natural Language Processing: Combining Parsers.
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Here NO counts the number of hypothesized constituents in the development set that match the binary predicate specified as an argument.
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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18 77.
They have made use of local and global features to deal with the instances of same token in a document.
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For every sequence of initial capitalized words, its longest substring that occurs in the same document as a sequence of initCaps is identified.
A beam search concept is applied as in speech recognition.
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(f1; ;mg n fl1; l2; l3g ;m) German to English the monotonicity constraint is violated mainly with respect to the German verbgroup.
In this paper the authors present a stochastic finite-state model for segmenting Chinese text into words.
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A greedy algorithm (or maximum-matching algorithm), GR: proceed through the sentence, taking the longest match with a dictionary entry at each point.
An extended lexicon model is defined, and its likelihood is compared to a baseline lexicon model, which takes only single-word dependencies into account.
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1 is given in Fig.
The evaluation compares the performance of the system with that of several human judges and inter-human agreement on a single correct way to segment a text.
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However, we have reason to doubt Chang et al.'s performance claims.
The evaluation compares the performance of the system with that of several human judges and inter-human agreement on a single correct way to segment a text.
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na me =>1 ha nzi fa mi ly 2 ha nzi gi ve n 3.
It is well-known that English constituency parsing models do not generalize to other languages and treebanks.
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The intuition here is that the role of a discourse marker can usually be de 9 Both the corpus split and pre-processing code are avail-.
Human judges also pointed out difficulties with the evaluation of long sentences.
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While the Bootstrap method is slightly more sensitive, it is very much in line with the sign test on text blocks.
From a DP-based solution to the traveling salesman problem, they present a novel technique to restrict the possible word reordering between source and target language in order to achieve an eÆcient search algorithm.
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2.1 Inverted Alignments.
The code is open source, has minimal dependencies, and offers both C++ and Java interfaces for integration.
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The trie data structure is commonly used for language modeling.
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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Since all long sentence translation are somewhat muddled, even a contrastive evaluation between systems was difficult.
They have made use of local and global features to deal with the instances of same token in a document.
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This group consists of (3 total number of possible zones) features.
The evaluation compares the performance of the system with that of several human judges and inter-human agreement on a single correct way to segment a text.
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The problem with these styles of evaluation is that, as we shall demonstrate, even human judges do not agree perfectly on how to segment a given text.
The model incorporates various recent techniques for incorporating and manipulating linguistic knowledge using finite-state transducers.
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The present proposal falls into the last group.
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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In these examples, the names identified by the two systems (if any) are underlined; the sentence with the correct segmentation is boxed.19 The differences in performance between the two systems relate directly to three issues, which can be seen as differences in the tuning of the models, rather than repre­ senting differences in the capabilities of the model per se.
This assumption, however, is not inherent to type-based tagging models.
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Here, we conThis model is equivalent to the standard HMM ex cept that it enforces the one-word-per-tag constraint.
Evaluation was done automatically using the BLEU score and manually on fluency and adequacy.
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While it is essential to be fluent in the target language, it is not strictly necessary to know the source language, if a reference translation was given.
In order to create good-sized vectors for similarity calculation, they had to set a high frequency threshold.
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We have checked if there are similar verbs in other major domains, but this was the only one.
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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This is consistent with the nature of these two settings: log-linear combination, which effectively takes the intersection of IN and OUT, does relatively better on NIST, where the domains are broader and closer together.
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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The bootstrap method has been critized by Riezler and Maxwell (2005) and Collins et al. (2005), as being too optimistic in deciding for statistical significant difference between systems.
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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Historically, Arabic grammar has identified two sentences types: those that begin with a nominal (� '.i �u _..
The authors show that PATB is similar to other tree-banks but that annotation consistency remains low.
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32 81.
They used graph-based label propagation for cross-lingual knowledge transfer and used the projected labels as features in an unsupervised model.
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To this end, we construct a bilingual graph over word types to establish a connection between the two languages (§3), and then use graph label propagation to project syntactic information from English to the foreign language (§4).
Their work is closely related to recent approaches that incorporate the sparsity constraint into the POS induction process.
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5.2 Setup.
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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Given counts cn1 where e.g. c1 is the vocabulary size, total memory consumption, in bits, is Our PROBING data structure places all n-grams of the same order into a single giant hash table.
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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To be short, we omit the target words e; e0 in the formulation of the search hypotheses.
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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They demonstrated this with the comparison of statistical systems against (a) manually post-edited MT output, and (b) a rule-based commercial system.
This topic has been getting more attention, driven by the needs of various NLP applications.
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3.3 Evaluation Results.
The approach has been successfully tested on the 8 000-word Verbmobil task.
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This approach leads to a search procedure with complexity O(E3 J4).
This topic has been getting more attention, driven by the needs of various NLP applications.
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If the expression is longer or complicated (like “A buys B” and “A’s purchase of B”), it is called “paraphrase”, i.e. a set of phrases which express the same thing or event.
The experimental tests are carried out on the Verbmobil task which is a limited-domain spoken-language task.
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10.
The use of global features has shown excellent result in the performance on MUC-6 and MUC-7 test data.
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For example, if a token starts with a capital letter and ends with a period (such as Mr.), then the feature InitCapPeriod is set to 1, etc. First Word: This feature group contains only one feature firstword.
This topic has been getting more attention, driven by the needs of various NLP applications.
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As can be seen in the example, the first two phrases have a different order of NE names from the last two, so we can determine that the last two phrases represent a reversed relation.
In this paper the authors present a stochastic finite-state model for segmenting Chinese text into words.
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constitute names, since we have only their segmentation, not the actual classification of the segmented words.
However, these approaches are ill-equipped to directly represent type-based constraints such as sparsity.
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For inference, we are interested in the posterior probability over the latent variables in our model.
The PROBING data structure uses linear probing hash tables and is designed for speed.
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The authors provided us with a ratio between TPT and SRI under different conditions. aLossy compression with the same weights. bLossy compression with retuned weights. ditions make the value appropriate for estimating repeated run times, such as in parameter tuning.
One can trivially create situations in which strictly binary-branching trees are combined to create a tree with only the root node and the terminal nodes, a completely flat structure.
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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.
In this paper, the authors proposed an approach for instance-weighting phrase pairs in an out-of-domain corpus in order to improve in-domain performance.
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This suggests a direct parallel to (1): where ˜p(s, t) is a joint empirical distribution extracted from the IN dev set using the standard procedure.2 An alternative form of linear combination is a maximum a posteriori (MAP) combination (Bacchiani et al., 2004).
The authors believe automatic paraphrase discovery is an important component for building a fully automatic information extraction system.
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This problem arises because our keywords consist of only one word.
Their results show that their high performance NER use less training data than other systems.
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The local features used are similar to those used in BBN' s IdentiFinder (Bikel et al., 1999) or MENE (Borthwick, 1999).
This topic has been getting more attention, driven by the needs of various NLP applications.
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While there are other obstacles to completing this idea, we believe automatic paraphrase discovery is an important component for building a fully automatic information extraction system.
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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We performed three experiments to evaluate our techniques.
It also incorporates the Good-Turing methodin estimating the likelihoods of previously unseen constructions, including morphological derivatives and personal names.
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However, we have reason to doubt Chang et al.'s performance claims.
They have made use of local and global features to deal with the instances of same token in a document.
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However, to classify a token , while Borthwick uses tokens from to (from two tokens before to two tokens after ), we used only the tokens , , and . Even with local features alone, MENERGI outperforms MENE (Borthwick, 1999).
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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Once the lexicon has been drawn, the model proceeds similarly to the standard token-level HMM: Emission parameters θ are generated conditioned on tag assignments T . We also draw transition parameters φ.
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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As noted for the perplexity task, we do not expect cache to grow substantially with model size, so RandLM remains a low-memory option.
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 natural language processing community is in the strong position of having many available approaches to solving some of its most fundamental problems.
The TRIE model uses less memory than the smallest lossless alternative and is still faster than SRILM.
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When two partial hypotheses have equal state (including that of other features), they can be recombined and thereafter efficiently handled as a single packed hypothesis.
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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To date we have not done a separate evaluation of foreign-name recognition.
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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Tables 4 and 5 show BABAR’s performance when just one contextual role knowledge source is used at a time.
Human judges also pointed out difficulties with the evaluation of long sentences.
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The best answer to this is: many research labs have very competitive systems whose performance is hard to tell apart.
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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Two of the Mainlanders also cluster close together but, interestingly, not particularly close to the Taiwan speakers; the third Mainlander is much more similar to the Taiwan speakers.
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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So we decided to use semantic class information only to rule out candidates.
The authors cluster NE instance pairs based on the words in the context using bag-of-words methods.
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If two phrases can be used to express the same relationship within an information extraction application (“scenario”), these two phrases are paraphrases.
However, using the top-level semantic classes of WordNet proved to be problematic as the class distinctions are too coarse.
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The anaphor and antecedent appear in boldface.
Their empirical results demonstrate that the type-based tagger rivals state-of-the-art tag-level taggers which employ more sophisticated learning mechanisms to exploit similar constraints.
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2 62.6 45.
The approach has been successfully tested on the 8 000-word Verbmobil task.
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Mai.
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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Our second contribution is to apply instance weighting at the level of phrase pairs.
Human judges also pointed out difficulties with the evaluation of long sentences.
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For statistics on this test set, refer to Figure 1.
It is well-known that English constituency parsing models do not generalize to other languages and treebanks.
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If we remove this sample from the evaluation, then the ATB type-level error rises to only 37.4% while the n-gram error rate increases to 6.24%.
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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Graph construction for structured prediction problems such as POS tagging is non-trivial: on the one hand, using individual words as the vertices throws away the context necessary for disambiguation; on the other hand, it is unclear how to define (sequence) similarity if the vertices correspond to entire sentences.
The AdaBoost algorithm was developed for supervised learning.
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We then discuss how we adapt and generalize a boosting algorithm, AdaBoost, to the problem of named entity classification.
The second algorithm builds on a boosting algorithm called AdaBoost.
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AdaBoost was first introduced in (Freund and Schapire 97); (Schapire and Singer 98) gave a generalization of AdaBoost which we will use in this paper.
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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The 3rd block contains the mixture baselines.