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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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We excluded these from the evaluation as they can be easily identified with a list of days/months.
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 each candidate antecedent, BABAR identifies the caseframe that would extract the candidate, pairs it with the anaphor’s caseframe, and consults the CF Network to see if this pair of caseframes has co-occurred in previous resolutions.
In order to create good-sized vectors for similarity calculation, they had to set a high frequency threshold.
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We are not claiming that this method is almighty.
It is well-known that English constituency parsing models do not generalize to other languages and treebanks.
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Manual annotation results in human in- terpretable grammars that can inform future tree- bank annotation decisions.
The manual evaluation of scoring translation on a graded scale from 1–5 seems to be very hard to perform.
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If one system is better in 95% of the sample sets, we conclude that its higher BLEU score is statistically significantly better.
The resulting model is compact, efficiently learnable and linguistically expressive.
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5 64.7 42.
It is well-known that English constituency parsing models do not generalize to other languages and treebanks.
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By establishing significantly higher parsing baselines, we have shown that Arabic parsing performance is not as poor as previously thought, but remains much lower than English.
The model incorporates various recent techniques for incorporating and manipulating linguistic knowledge using finite-state transducers.
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Particular relations are also consistent with particular hypotheses about the segmentation of a given sentence, and the scores for particular relations can be incremented or decremented depending upon whether the segmentations with which they are consistent are "popular" or not.
This paper talks about Pseudo-Projective Dependency Parsing.
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In order to facilitate this task, we extend the set of arc labels to encode information about lifting operations.
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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If we were working with more than three parsers we could investigate minority constituents, those constituents that are suggested by at least one parser, but which the majority of the parsers do not suggest.
Evaluation was done automatically using the BLEU score and manually on fluency and adequacy.
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The other half was replaced by other participants, so we ended up with roughly the same number.
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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The judgements tend to be done more in form of a ranking of the different systems.
It also incorporates the Good-Turing methodin estimating the likelihoods of previously unseen constructions, including morphological derivatives and personal names.
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For.
The departure from the traditional token-based tagging approach allow them to explicitly capture type-level distributional properties of valid POS tag assignments as part of the model.
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2 61.7 64.
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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Topicalization of NP subjects in SVO configurations causes confusion with VO (pro-drop).
The AdaBoost algorithm was developed for supervised learning.
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This left 962 examples, of which 85 were noise.
This paper talks about KenLM: Faster and Smaller Language Model Queries.
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Figure 1 shows an example.
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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Table 3 Classes of words found by ST for the test corpus.
Human judges also pointed out difficulties with the evaluation of long sentences.
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If one system is better in 95% of the sample sets, we conclude that its higher BLEU score is statistically significantly better.
The PROBING data structure uses linear probing hash tables and is designed for speed.
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Time starts when Moses is launched and therefore includes model loading time.
This paper discusses the Potsdam Commentary Corpus, a corpus of german assembeled by potsdam university.
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rhetorical analysis We are experimenting with a hybrid statistical and knowledge-based system for discourse parsing and summarization (Stede 2003), (Hanneforth et al. 2003), again targeting the genre of commentaries.
The model incorporates various recent techniques for incorporating and manipulating linguistic knowledge using finite-state transducers.
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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.
All the texts were annotated by two people.
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There are still some open issues to be resolved with the format, but it represents a first step.
They found replacing it with a ranked evaluation to be more suitable.
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Pairwise comparison: We can use the same method to assess the statistical significance of one system outperforming another.
However, using the top-level semantic classes of WordNet proved to be problematic as the class distinctions are too coarse.
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4 Evaluation Results.
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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with the number of exactly matching guess trees.
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 summarized in Equation 5.
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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Following the setup of Johnson (2007), we use the whole of the Penn Treebank corpus for training and evaluation on English.
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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Bikel et al.
Koehn and Monz carried out an extensive manual and automatic evaluation of machine translation performance on European language pairs.
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For the automatic scoring method BLEU, we can distinguish three quarters of the systems.
The experimental tests are carried out on the Verbmobil task which is a limited-domain spoken-language task.
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The final score is obtained from: max e;e0 j2fJ􀀀L;;Jg p($je; e0) Qe0 (e; I; f1; ; Jg; j); where p($je; e0) denotes the trigram language model, which predicts the sentence boundary $ at the end of the target sentence.
In this paper the authors present a stochastic finite-state model for segmenting Chinese text into words.
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10 Here we use the Good-Turing estimate (Baayen 1989; Church and Gale 1991), whereby the aggregate probability of previously unseen instances of a construction is estimated as ni/N, where N is the total number of observed tokens and n1 is the number of types observed only once.
They showed the efficacy of graph-based label propagation for projecting part-of-speech information across languages.
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To establish a soft correspondence between the two languages, we use a second similarity function, which leverages standard unsupervised word alignment statistics (§3.3).3 Since we have no labeled foreign data, our goal is to project syntactic information from the English side to the foreign side.
It also incorporates the Good-Turing methodin estimating the likelihoods of previously unseen constructions, including morphological derivatives and personal names.
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Word frequencies are estimated by a re-estimation procedure that involves apply­ ing the segmentation algorithm presented here to a corpus of 20 million words,8 using 8 Our training corpus was drawn from a larger corpus of mixed-genre text consisting mostly of.
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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Our original hope in combining these parsers is that their errors are independently distributed.
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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In general, m; l; l0 6= fl1; l2; l3g and in line umber 3 and 4, l0 must be chosen not to violate the above reordering restriction.
This paper talks about KenLM: Faster and Smaller Language Model Queries.
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Overall, language modeling significantly impacts decoder performance.
The model incorporates various recent techniques for incorporating and manipulating linguistic knowledge using finite-state transducers.
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The first probability is estimated from a name count in a text database, and the rest of the probabilities are estimated from a large list of personal names.n Note that in Chang et al.'s model the p(rule 9) is estimated as the product of the probability of finding G 1 in the first position of a two-hanzi given name and the probability of finding G2 in the second position of a two-hanzi given name, and we use essentially the same estimate here, with some modifications as described later on.
The approach assumes that the word reordering is restricted to a few positions in the source sentence.
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f1; ; Jg denotes a coverage set including all positions from the starting position 1 to position J and j 2 fJ 􀀀L; ; Jg.
Here both parametric and non-parametric models are explored.
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The standard measures for evaluating Penn Treebank parsing performance are precision and recall of the predicted constituents.
Replacing this with a ranked evaluation seems to be more suitable.
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Due to many similarly performing systems, we are not able to draw strong conclusions on the question of correlation of manual and automatic evaluation metrics.
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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We also mark all tags that dominate a word with the feminine ending :: taa mar buuTa (markFeminine).
Finally, several coreference systems have successfully incorporated anaphoricity determination modules.
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The learned information was recycled back into the resolver to improve its performance.
The texts were annotated with the RSTtool.
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3.5 Improved models of discourse.
Their results show that their high performance NER use less training data than other systems.
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On the other hand, if it is seen as McCann Pte.
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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In addition, this formulation results in a dramatic reduction in the number of model parameters thereby, enabling unusually rapid training.
It is probably the first analysis of Arabic parsing of this kind.
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Richer tag sets have been suggested for modeling morphologically complex distinctions (Diab, 2007), but we find that linguistically rich tag sets do not help parsing.
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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If so, the CF Network reports that the anaphor and candidate may be coreferent.
The second algorithm builds on a boosting algorithm called AdaBoost.
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(Specifically, the limit n starts at 5 and increases by 5 at each iteration.)
The TRIE model uses less memory than the smallest lossless alternative and is still faster than SRILM.
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Process statistics are already collected by the kernel (and printing them has no meaningful impact on performance).
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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Table 2 shows our complete set of results.
They found replacing it with a ranked evaluation to be more suitable.
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In the graphs, system scores are indicated by a point, the confidence intervals by shaded areas around the point.
Evaluation was done automatically using the BLEU score and manually on fluency and adequacy.
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On the other hand, when all systems produce muddled output, but one is better, and one is worse, but not completely wrong, a judge is inclined to hand out judgements of 4, 3, and 2.
The corpus was annoted with different linguitic information.
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The paper explains the design decisions taken in the annotations, and describes a number of applications using this corpus with its multi-layer annotation.
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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Although this property is not structural, it depends on the structural property that sentences can be built from a finite set of clauses of bounded structure as noted by Joshi (1983/85).
The authors believe automatic paraphrase discovery is an important component for building a fully automatic information extraction system.
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We propose an unsupervised method to discover paraphrases from a large untagged corpus, without requiring any seed phrase or other cue.
These clusters are computed using an SVD variant without relying on transitional structure.
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5.2 Setup.
The corpus was annoted with different linguitic information.
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Commentaries argue in favor of a specific point of view toward some political issue, often dicussing yet dismissing other points of view; therefore, they typically offer a more interesting rhetorical structure than, say, narrative text or other portions of newspapers.
This paper talks about Exploiting Diversity in Natural Language Processing: Combining Parsers.
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Another technique for parse hybridization is to use a naïve Bayes classifier to determine which constituents to include in the parse.
It also incorporates the Good-Turing methodin estimating the likelihoods of previously unseen constructions, including morphological derivatives and personal names.
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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.
Replacing this with a ranked evaluation seems to be more suitable.
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Annotators suggested that long sentences are almost impossible to judge.
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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computing the recall of the other's judgments relative to this standard.
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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It is a sequence of proper nouns within an NP; its last word Cooper is the head of the NP; and the NP has an appositive modifier (a vice president at S.&P.) whose head is a singular noun (president).
The bias of automatic methods in favour of statistical systems seemed to be less pronounced on out-of-domain test data.
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However, ince we extracted the test corpus automatically from web sources, the reference translation was not always accurate — due to sentence alignment errors, or because translators did not adhere to a strict sentence-by-sentence translation (say, using pronouns when referring to entities mentioned in the previous sentence).
They showed better grammars to improve performance on both morphological and syntactic tasks, providing support for the advantage of a joint framework over pipelined or factorized ones.
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The implication of this ambiguity for a parser is that the yield of syntactic trees no longer consists of spacedelimited tokens, and the expected number of leaves in the syntactic analysis in not known in advance.
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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We report token- and type-level accuracy in Table 3 and 6 for all languages and system settings.
The authors in this paper describe a search procedure for statistical machine translation (MT) based on dynamic programming (DP).
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For Æ = 0, no new target word is generated, while an additional source sentence position is covered.
The approach assumes that the word reordering is restricted to a few positions in the source sentence.
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In Section 4, we present the performance measures used and give translation results on the Verbmobil task.
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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Systems that generally do better than others will receive a positive average normalizedjudgement per sentence.
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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Figure 4 shows a constituent headed by a process nominal with an embedded adjective phrase.
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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This is because in many cases, the use of hyphens can be considered to be optional (e.g., third-quarter or third quarter).
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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We tagged each noun with the top-level semantic classes assigned to it in Word- Net.
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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For example, in MUC6, there are four zones (TXT, HL, DATELINE, DD).
The approach has been successfully tested on the 8 000-word Verbmobil task.
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The translation of one position in the source sentence may be postponed for up to L = 3 source positions, and the translation of up to two source positions may be anticipated for at most R = 10 source positions.
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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We train and test on the CoNLL-X training set.
The authors in this paper describe a search procedure for statistical machine translation (MT) based on dynamic programming (DP).
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Table 3: Training and test conditions for the Verbmobil task (*number of words without punctuation marks).
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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As we shall argue, the semantic class affiliation of a hanzi constitutes useful information in predicting its properties.
A beam search concept is applied as in speech recognition.
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Sie.
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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They are: 5We are grateful to an anonymous reviewer for pointing this out.
they evaluate various specific aspects of the segmentation, as well as the overall segmentation performance.
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Mutual information was shown to be useful in the segmentation task given that one does not have a dictionary.
The manual evaluation of scoring translation on a graded scale from 1–5 seems to be very hard to perform.
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At the very least, we are creating a data resource (the manual annotations) that may the basis of future research in evaluation metrics.
Evaluation was done automatically using the BLEU score and manually on fluency and adequacy.
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We dropped, however, one of the languages, Finnish, partly to keep the number of tracks manageable, partly because we assumed that it would be hard to find enough Finnish speakers for the manual evaluation.
It also incorporates the Good-Turing methodin estimating the likelihoods of previously unseen constructions, including morphological derivatives and personal names.
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immediately by a Romanization into the pinyin transliteration scheme; numerals following each pinyin syllable represent tones.
However, using the top-level semantic classes of WordNet proved to be problematic as the class distinctions are too coarse.
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3 The Coreference Resolution Model.
The experimental tests are carried out on the Verbmobil task which is a limited-domain spoken-language task.
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The restriction can be expressed in terms of the number of uncovered source sentence positions to the left of the rightmost position m in the coverage set.
However, using the top-level semantic classes of WordNet proved to be problematic as the class distinctions are too coarse.
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However, using the top-level semantic classes of WordNet proved to be problematic because the class distinctions are too coarse.
BABAR's performance in both domains of terrorism and natural disaster, and the contextual-role knowledge in pronouns have shown successful results.
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Our representation of contextual roles is based on information extraction patterns that are converted into simple caseframes.
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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However, in existing systems, this expansion come with a steep increase in model complexity.
Their work is closely related to recent approaches that incorporate the sparsity constraint into the POS induction process.
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However, these approaches are ill-equipped to directly represent type-based constraints such as sparsity.
The code is open source, has minimal dependencies, and offers both C++ and Java interfaces for integration.
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Values in the trie are minimally sized at the bit level, improving memory consumption over trie implementations in SRILM, IRSTLM, and BerkeleyLM.
All the texts were annotated by two people.
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This fact annoyed especially his dog...).
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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Microsoft’s approach uses dependency trees, others use hierarchical phrase models.
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 are currently working on a complete open source implementation of a training and decoding system, which should become available over the summer. pus, from which also the in-domain test set is taken.
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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(2006) developed a technique for splitting and chunking long sentences.
They have made use of local and global features to deal with the instances of same token in a document.
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In Section 5, we try to compare results of MENE, IdentiFinder, and MENERGI.
In this paper the author evaluates machine translation performance for six European language pairs that participated in a shared task: translating French, German, Spanish texts to English and back.
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The other half was replaced by other participants, so we ended up with roughly the same number.
This paper discusses the Potsdam Commentary Corpus, a corpus of german assembeled by potsdam university.
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Since 170 annotated texts constitute a fairly small training set, Reitter found that an overall recognition accuracy of 39% could be achieved using his method.
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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Handling Unknown tokens When handling unknown tokens in a language such as Hebrew various important aspects have to be borne in mind.
Two general approaches are presented and two combination techniques are described for each approach.
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We include a constituent in our hypothesized parse if it appears in the output of a majority of the parsers.
In order to create good-sized vectors for similarity calculation, they had to set a high frequency threshold.
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Although this is not a precise criterion, most cases we evaluated were relatively clear-cut.