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Evaluation was done automatically using the BLEU score and manually on fluency and adequacy.
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Figure 1 provides some statistics about this corpus.
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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can expect famous names like Zhou Enlai's to be in many dictionaries, but names such as :fi lf;f; shi2jil-lin2, the name of the second author of this paper, will not be found in any dictionary.
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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3 60.7 50.
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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For each resolution in the training data, BABAR also associates the co-referring expression of an NP with the NP’s caseframe.
The overall performance of their joint framework demonstrated that a probability distribution obtained over mere syntactic contexts using a Treebank grammar and a data-driven lexicon outperformed upper bounds proposed by previous joint disambiguation systems and achieved segmentation and parsing results on a par with state-of-the-art standalone applications results.
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The form mnh itself can be read as at least three different verbs (“counted”, “appointed”, “was appointed”), a noun (“a portion”), and a possessed noun (“her kind”).
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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Finally, other packages implement language model estimation while we are currently dependent on them to generate an ARPA file.
the "Potsdam Commentary Corpus" or PCC consists of 170 commentaries from Ma¨rkische Allgemeine Zeitung, a German regional daily.
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5 ‘Underspecified Rhetorical Markup Language’ 6 This confirms the figure given by (Schauer, Hahn.
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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2.2 Contextual Role Knowledge.
Their results show that their high performance NER use less training data than other systems.
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However, it is unlikely that other occurrences of News Broadcasting Corp. in the same document also co-occur with Even.
The resulting model is compact, efficiently learnable and linguistically expressive.
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(2010)’s richest model: optimized via either EM or LBFGS, as their relative performance depends on the language.
It is well-known that English constituency parsing models do not generalize to other languages and treebanks.
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96 75.
This paper discusses the Potsdam Commentary Corpus, a corpus of german assembeled by potsdam university.
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We will briefly discuss this point in Section 3.1.
All the texts were annotated by two people.
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This paper, however, provides a comprehensive overview of the data collection effort and its current state.
The problem of coreference resolution has received considerable attention, including theoretical discourse models and supervised machine learning systems.
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Using this heuristic, BABAR identifies existential definite NPs in the training corpus using our previous learning algorithm (Bean and Riloff, 1999) and resolves all occurrences of the same existential NP with each another.1 2.1.2 Syntactic Seeding BABAR also uses syntactic heuristics to identify anaphors and antecedents that can be easily resolved.
The overall parsing accuracy obtained with the pseudo-projective approach is still lower than for the best projective parsers.
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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.
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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Each unlabeled pair (x1,i, x2,i) is represented as an edge between nodes corresponding to x1,i and X2,i in the graph.
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 sign test checks, how likely a sample of better and worse BLEU scores would have been generated by two systems of equal performance.
The problem of coreference resolution has received considerable attention, including theoretical discourse models and supervised machine learning systems.
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For each co-occurrence relation (noun/caseframe for CFLex, and caseframe/caseframe for CFNet), BABAR computes its log-likelihood value and looks it up in the χ2 table to obtain a confidence level.
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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The authors acknowledge the support of the NSF (CAREER grant IIS0448168, and grant IIS 0904684).
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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Reflexive pronouns with only 1 NP in scope..
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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We experimented with increasingly rich grammars read off of the treebank.
This assumption, however, is not inherent to type-based tagging models.
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In contrast to the Bayesian HMM, θt is not drawn from a distribution which has support for each of the n word types.
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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logical rules, and personal names; the transitive closure of the resulting machine is then computed.
It is annotated with several data: morphology, syntax, rhetorical structure, connectors, correference and informative structure.
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The price shocked her.), or same-kind (e.g., Her health insurance paid for the hospital fees, but the automobile insurance did not cover the repair.).
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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All annotations are done with specific tools and in XML; each layer has its own DTD.
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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While the proportion of sentences containing non-projective dependencies is often 15–25%, the total proportion of non-projective arcs is normally only 1–2%.
There is no global pruning.
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This work has been supported as part of the Verbmobil project (contract number 01 IV 601 A) by the German Federal Ministry of Education, Science, Research and Technology and as part of the Eutrans project (ESPRIT project number 30268) by the European Community.
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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Annotators suggested that long sentences are almost impossible to judge.
In this paper the authors present a stochastic finite-state model for segmenting Chinese text into words.
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Church and Hanks [1989]), and we have used lists of character pairs ranked by mutual information to expand our own dictionary.
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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with the number of exactly matching guess trees.
The first method builds on results from (Yarowsky 95) and (Blum and Mitchell 98).
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(7), such as the likelihood function used in maximum-entropy problems and other generalized additive models (Lafferty 99).
The code is open source, has minimal dependencies, and offers both C++ and Java interfaces for integration.
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These are written to the state s(wn1) and returned so that they can be used for the following query.
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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For the translation model Pr(fJ 1 jeI 1), we go on the assumption that each source word is aligned to exactly one target word.
It is probably the first analysis of Arabic parsing of this kind.
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4 Traditional Arabic linguistic theory treats both of these types as subcategories of noun � '.i . Figure 1: The Stanford parser (Klein and Manning, 2002) is unable to recover the verbal reading of the unvocalized surface form 0 an (Table 1).
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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The number of latent HMM states for each language in our experiments was set to the number of fine tags in the language’s treebank.
The corpus was annoted with different linguitic information.
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html 4 www.wagsoft.com/RSTTool assigning rhetorical relations is a process loaded with ambiguity and, possibly, subjectivity.
Here both parametric and non-parametric models are explored.
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Three state-of-the-art statistical parsers are combined to produce more accurate parses, as well as new bounds on achievable Treebank parsing accuracy.
Finally, several coreference systems have successfully incorporated anaphoricity determination modules.
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The F- measure score increased for both domains, reflecting a substantial increase in recall with a small decrease in precision.
Their results show that their high performance NER use less training data than other systems.
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If is unique, then a feature (Unique, Zone) is set to 1, where Zone is the document zone where appears.
The code is open source, has minimal dependencies, and offers both C++ and Java interfaces for integration.
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This causes a problem for reverse trie implementations, including SRILM itself, because it leaves n+1-grams without an n-gram node pointing to them.
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 set is then compared with the set generated from the Penn Treebank parse to determine the precision and recall.
The use of global features has shown excellent result in the performance on MUC-6 and MUC-7 test data.
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The overall performance of the LTG system was outstanding, but the system consists of a sequence of many hand-coded rules and machine-learning modules.
Here we show how non-projective dependency parsing can be achieved by combining a data driven projective parser with special graph transformation techniques.
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Although the best published results for the Collins parser is 80% UAS (Collins, 1999), this parser reaches 82% when trained on the entire training data set, and an adapted version of Charniak’s parser (Charniak, 2000) performs at 84% (Jan Hajiˇc, pers. comm.).
The contextual rules are restricted and may not be applicable to every example, but the spelling rules are generally applicable and should have good coverage.
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The first unsupervised algorithm we describe is based on the decision list method from (Yarowsky 95).
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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For each caseframe, BABAR collects the semantic classes associated with the head nouns of NPs that were extracted by the caseframe.
Finally, several coreference systems have successfully incorporated anaphoricity determination modules.
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In this case, this knowledge source reports that the candidate is not a viable antecedent for the anaphor.
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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Participants were also provided with two sets of 2,000 sentences of parallel text to be used for system development and tuning.
It is well-known that English constituency parsing models do not generalize to other languages and treebanks.
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Even with vocalization, there are linguistic categories that are difficult to identify without semantic clues.
In order to create good-sized vectors for similarity calculation, they had to set a high frequency threshold.
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shows some keywords with their scores.
They proposed an unsupervised method to discover paraphrases from a large untagged corpus.
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First, we will describe their method and compare it with our method.
They proposed a single joint model for performing both morphological segmentation and syntactic disambiguation which bypasses the associated circularity.
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An additional case of super-segmental morphology is the case of Pronominal Clitics.
It also incorporates the Good-Turing methodin estimating the likelihoods of previously unseen constructions, including morphological derivatives and personal names.
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Obviously, the presence of a title after a potential name N increases the probability that N is in fact a name.
Koehn and Monz carried out an extensive manual and automatic evaluation of machine translation performance on European language pairs.
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This is not completely surprising, since all systems use very similar technology.
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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3 Throughout this paper we shall give Chinese examples in traditional orthography, followed.
They found replacing it with a ranked evaluation to be more suitable.
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There may be occasionally a system clearly at the top or at the bottom, but most systems are so close that it is hard to distinguish them.
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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36.
This paper presents methods to query N-gram language models, minimizing time and space costs.
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This paper describes the several performance techniques used and presents benchmarks against alternative implementations.
The TRIE model uses less memory than the smallest lossless alternative and is still faster than SRILM.
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Figure 2 shows timing results.
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 first shows how constituent features and context do not help in deciding which parser to trust.
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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VBD she added VP PUNC “ SBAR IN NP 0 NN.
Their work is closely related to recent approaches that incorporate the sparsity constraint into the POS induction process.
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2 62.2 39.
This paper presents methods to query N-gram language models, minimizing time and space costs.
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The code is opensource, has minimal dependencies, and offers both C++ and Java interfaces for integration.
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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Je voudrais pr´eciser, a` l’adresse du commissaire Liikanen, qu’il n’est pas ais´e de recourir aux tribunaux nationaux.
The resulting model is compact, efficiently learnable and linguistically expressive.
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3 54.4 33.
Most IE researchers have been creating paraphrase knowledge by hand and specific tasks.
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All the links in the “CC-domain are shown in Step 4 in subsection 3.2.
The contextual rules are restricted and may not be applicable to every example, but the spelling rules are generally applicable and should have good coverage.
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Unfortunately, modifying the model to account for these kind of dependencies is not at all straightforward.
The approach has been successfully tested on the 8 000-word Verbmobil task.
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(S; C; j); Not only the coverage set C and the positions j; j0, but also the verbgroup states S; S0 are taken into account.
Their results show that their high performance NER use less training data than other systems.
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Although we have not done any experiments on other languages, this way of using global features from a whole document should be applicable to other languages.
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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Instead of offsetting new topics with punctuation, writers of MSA in sert connectives such as � wa and � fa to link new elements to both preceding clauses and the text as a whole.
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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The linear LM (lin lm), TM (lin tm) and MAP TM (map tm) used with non-adapted counterparts perform in all cases slightly worse than the log-linear combination, which adapts both LM and TM components.
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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37 79.
They proposed a single joint model for performing both morphological segmentation and syntactic disambiguation which bypasses the associated circularity.
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(we ignored the 419 trees in their development set.)
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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We 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.
The first method builds on results from (Yarowsky 95) and (Blum and Mitchell 98).
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The core of Yarowsky's algorithm is as follows: where h is defined by the formula in equation 2, with counts restricted to training data examples that have been labeled in step 2.
They focused on phrases which two Named Entities, and proceed in two stages.
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We will return to these issues in the discussion section.
They focused on phrases which two Named Entities, and proceed in two stages.
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As lower frequency examples include noise, we set a threshold that an NE category pair should appear at least 5 times to be considered and an NE instance pair should appear at least twice to be considered.
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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In this shared task, we were also confronted with this problem, and since we had no funding for paying human judgements, we asked participants in the evaluation to share the burden.
The manual evaluation of scoring translation on a graded scale from 1–5 seemed to be very hard to perform.
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See Figure 3 for a screenshot of the evaluation tool.
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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A few annotators suggested to break up long sentences into clauses and evaluate these separately.
This corpus has several advantages: it is annotated at different levels.
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For all these annotation taks, G¨otze developed a series of questions (essentially a decision tree) designed to lead the annotator to the ap propriate judgement.
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 each pair of judges, consider one judge as the standard,.
The authors show that PATB is similar to other tree-banks but that annotation consistency remains low.
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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.
They found replacing it with a ranked evaluation to be more suitable.
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This data set of manual judgements should provide a fruitful resource for research on better automatic scoring methods.
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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The sign test checks, how likely a sample of better and worse BLEU scores would have been generated by two systems of equal performance.
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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Caching for IRSTLM is smaller at 0.09 GB resident memory, though it supports only a single thread.
This paper talks about Unsupervised Models for Named Entity Classification.
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Each vertex within a connected component must have the same label — in the binary classification case, we need a single labeled example to identify which component should get which label.
They plan on extending instance-weighting to other standard SMT components and capture the degree of generality of phrase pairs.
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It is difficult when IN and OUT are dissimilar, as they are in the cases we study.
This paper presents methods to query N-gram language models, minimizing time and space costs.
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Our test machine has two Intel Xeon E5410 processors totaling eight cores, 32 GB RAM, and four Seagate Barracuda disks in software RAID 0 running Linux 2.6.18.
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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Systems that generally do worse than others will receive a negative one.
This paper talks about Exploiting Diversity in Natural Language Processing: Combining Parsers.
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The resulting parsers surpass the best previously published performance results for the Penn Treebank.
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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This heuristics is used to prune all segmentation possibilities involving “lexically improper” segments.
This paper talks about Pseudo-Projective Dependency Parsing.
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By contrast, when we turn to a comparison of the three encoding schemes it is hard to find any significant differences, and the overall impression is that it makes little or no difference which encoding scheme is used, as long as there is some indication of which words are assigned their linear head instead of their syntactic head by the projective parser.
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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Initially, the DempsterShafer model assumes that all hypotheses are equally likely, so it creates a set called θ that includes all hypotheses.
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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These two properties of the tree sets are not only linguistically relevant, but also have computational importance.
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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Previous work on morphological and syntactic disambiguation in Hebrew used different sets of data, different splits, differing annotation schemes, and different evaluation measures.
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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LCFRS's share several properties possessed by the class of mildly context-sensitive formalisms discussed by Joshi (1983/85).
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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Following Sproat and Shih (1990), performance for Chinese segmentation systems is generally reported in terms of the dual measures of precision and recalP It is fairly standard to report precision and recall scores in the mid to high 90% range.
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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A straightforward way to find the shortest tour is by trying all possible permutations of the n cities.
The authors show that PATB is similar to other tree-banks but that annotation consistency remains low.
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Also surprising is the low test set OOV rate given the possibility of morphological variation in Arabic.
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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3.