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backsum

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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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Previous work on morphological and syntactic disambiguation in Hebrew used different sets of data, different splits, differing annotation schemes, and different evaluation measures.
Finally, several coreference systems have successfully incorporated anaphoricity determination modules.
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The types of patterns produced by AutoSlog are outlined in (Riloff, 1996).
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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Most computational models for coreference resolution rely on properties of the anaphor and candidate antecedent, such as lexical matching, grammatical and syntactic features, semantic agreement, and positional information.
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 group of features attempts to capture such information.
The texts were annotated with the RSTtool.
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Assigning rhetorical relations thus poses questions that can often be answered only subjectively.
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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Annotators suggested that long sentences are almost impossible to judge.
The authors in this paper describe a search procedure for statistical machine translation (MT) based on dynamic programming (DP).
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The experimental tests are carried out on the Verbmobil task (GermanEnglish, 8000-word vocabulary), which is a limited-domain spoken-language task.
The first method builds on results from (Yarowsky 95) and (Blum and Mitchell 98).
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(We would like to note though that unlike previous boosting algorithms, the CoBoost algorithm presented here is not a boosting algorithm under Valiant's (Valiant 84) Probably Approximately Correct (PAC) model.)
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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If enough parsers suggest that a particular constituent belongs in the parse, we include it.
The authors cluster NE instance pairs based on the words in the context using bag-of-words methods.
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Also, expanding on the techniques for the automatic generation of extraction patterns (Riloff 96; Sudo 03) using our method, the extraction patterns which have the same meaning can be automatically linked, enabling us to produce the final table fully automatically.
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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Also, the argument has been made that machine translation performance should be evaluated via task-based evaluation metrics, i.e. how much it assists performing a useful task, such as supporting human translators or aiding the analysis of texts.
This paper presents a maximum entropy-based named entity recognizer (NER).
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Another attempt at using global information can be found in (Borthwick, 1999).
It is probably the first analysis of Arabic parsing of this kind.
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(b) POS tagging accuracy is lowest for maSdar verbal nouns (VBG,VN) and adjectives (e.g., JJ).
Bean and Riloff also used bootstrapping to extend their semantic compatibility model, proposed using caseframe network for anaphora resolution, information extraction patterns to identify contextual clues for determining compatibility between NPs.
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BABAR achieved recall in the 4250% range for both domains, with 76% precision overall for terrorism and 87% precision for natural disasters.
The authors believe automatic paraphrase discovery is an important component for building a fully automatic information extraction system.
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However, there are phrases which express the same meanings even though they do not share the same keyword.
Using less training data than other systems, their NER can perform as well as other state-of-the-art NERs.
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By making use of global context, it has achieved excellent results on both MUC6 and MUC7 official test data.
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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M(wi) = Li).
The approach has been successfully tested on the 8 000-word Verbmobil task.
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The proof is given in (Tillmann, 2000).
On the basis of that observation, they described a class of formalisms which they called Linear Context- Free Rewriting Systems (LCFRs), and showed they were recognizable in polynomial time and generated only semilinear languages.
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A multicomponent Tree Adjoining Grammar (MCTAG) consists of a finite set of finite elementary tree sets.
The model incorporates various recent techniques for incorporating and manipulating linguistic knowledge using finite-state transducers.
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nan2gual 'pumpkin.'
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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lla/llb and 14a/14b respectively).
The first method builds on results from (Yarowsky 95) and (Blum and Mitchell 98).
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The approach builds from an initial seed set for a category, and is quite similar to the decision list approach described in (Yarowsky 95).
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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In our model, however, all lattice paths are taken to be a-priori equally likely.
Replacing this with a ranked evaluation seems to be more suitable.
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While we had up to 11 submissions for a translation direction, we did decide against presenting all 11 system outputs to the human judge.
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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Removing the leaves from the resulting tree yields a parse for L under G, with the desired probabilities.
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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A contextual rule considers words surrounding the string in the sentence in which it appears (e.g., a rule that any proper name modified by an appositive whose head is president is a person).
The fact that projective dependency parsers can never exactly reproduce the analyses found in non-projective tree banks is often neglected because of the relative scarcity of problematic constructions.
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As observed by Kahane et al. (1998), any (nonprojective) dependency graph can be transformed into a projective one by a lifting operation, which replaces each non-projective arc wj wk by a projective arc wi —* wk such that wi —*∗ wj holds in the original graph.
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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This representation of structures by substrings and the composition operation by its effect on substrings is related to the work of Rounds (1985).
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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8 57.3 +F EA TS be st me dia n 50.
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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The approach recursively evaluates a quantity Q(C; j), where C is the set of already visited cities and sj is the last visited city.
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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If “gun” and “revolver” refer to the same object, then it should also be acceptable to say that Fred was “killed with a gun” and that the burglar “fireda revolver”.
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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One annotator suggested that this was the case for as much as 10% of our test sentences.
It is well-known that English constituency parsing models do not generalize to other languages and treebanks.
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In general, several gross corpus statistics favor the ATB, so other factors must contribute to parsing underperformance.
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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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.
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 unaware of prior results for the Stanford parser.
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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Each word is terminated by an arc that represents the transduction between f and the part of speech of that word, weighted with an estimated cost for that word.
However, these approaches are ill-equipped to directly represent type-based constraints such as sparsity.
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3 58.3 40.
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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However, it is robust, efficient, and easy to implement.4 To perform the maximization in (7), we used the popular L-BFGS algorithm (Liu and Nocedal, 1989), which requires gradient information.
They proposed an unsupervised method to discover paraphrases from a large untagged corpus.
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If a phrase does not contain any keywords, the phrase is discarded.
The TRIE model uses less memory than the smallest lossless alternative and is still faster than SRILM.
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Many packages perform language model queries.
It is annotated with several data: morphology, syntax, rhetorical structure, connectors, correference and informative structure.
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Here, annotation proceeds in two phases: first, the domains and the units of IS are marked as such.
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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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.
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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It also incorporates the Good-Turing method (Baayen 1989; Church and Gale 1991) in estimating the likelihoods of previously unseen con­ structions, including morphological derivatives and personal names.
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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During the search process, a partial hypothesis is extended by choosing a source sentence position, which has not been aligned with a target sentence position yet.
It is probably the first analysis of Arabic parsing of this kind.
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01 75.
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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Here we do not submit to this view.
Finally, several coreference systems have successfully incorporated anaphoricity determination modules.
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BABAR uses unsupervised learning to acquire this knowledge from plain text without the need for annotated training data.
This corpus has several advantages: it is annotated at different levels.
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Nonetheless, the prospect of a network of annotated discourse resources seems particularly promising if not only a single annotation layer is used but a whole variety of them, so that a systematic search for correlations between them becomes possible, which in turn can lead to more explanatory models of discourse structure.
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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73 81.
They plan on extending instance-weighting to other standard SMT components and capture the degree of generality of phrase pairs.
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Matsoukas et al (2009) generalize it by learning weights on sentence pairs that are used when estimating relative-frequency phrase-pair probabilities.
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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— similar results have been observed across multiple languages.
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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On a set of 11 sentence fragments-the A set-where they reported 100% recall and precision for name identification, we had 73% recall and 80% precision.
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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This process produces a large set of caseframes coupled with a list of the noun phrases that they extracted.
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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Only IRSTLM does not support threading.
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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See Figure 3 for a screenshot of the evaluation tool.
Their work is closely related to recent approaches that incorporate the sparsity constraint into the POS induction process.
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1 61.7 37.
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 machine learning community has been in a similar situation and has studied the combination of multiple classifiers (Wolpert, 1992; Heath et al., 1996).
The TRIE model uses less memory than the smallest lossless alternative and is still faster than SRILM.
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We incur some additional memory cost due to storing state in each hypothesis, though this is minimal compared with the size of the model itself.
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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The DempsterShafer rule for combining pdfs is: to {C}, meaning that it is 70% sure the correct hypothesis is C. The intersection of these sets is the null set because these beliefs are contradictory.
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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A final alternate approach would be to combine weighted joint frequencies rather than conditional estimates, ie: cI(s, t) + w,\(s, t)co(, s, t), suitably normalized.5 Such an approach could be simulated by a MAP-style combination in which separate 0(t) values were maintained for each t. This would make the model more powerful, but at the cost of having to learn to downweight OUT separately for each t, which we suspect would require more training data for reliable performance.
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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gaolxing4 'happy' => F.i'JF.i'J Jl!
This paper talks about Unsupervised Models for Named Entity Classification.
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As in boosting, the algorithm works in rounds.
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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May 1995).
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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6 Our knowledge sources return some sort of probability estimate, although in some cases this estimate is not especially well-principled (e.g., the Recency KS).
Here both parametric and non-parametric models are explored.
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This work was funded by NSF grant IRI-9502312.
This topic has been getting more attention, driven by the needs of various NLP applications.
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We proposed an unsupervised method to discover paraphrases from a large untagged corpus.
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 may be the sign of a maturing research environment.
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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The basic word order is VSO, but SVO, VOS, and VO configurations are also possible.2 Nouns and verbs are created by selecting a consonantal root (usually triliteral or quadriliteral), which bears the semantic core, and adding affixes and diacritics.
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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Recent work by Finkel and Manning (2009) which re-casts Daum´e’s approach in a hierarchical MAP framework may be applicable to this problem.
The texts were annotated with the RSTtool.
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3.4 Salience-based text generation.
This assumption, however, is not inherent to type-based tagging models.
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We hypothesize that modeling morphological information will greatly constrain the set of possible tags, thereby further refining the representation of the tag lexicon.
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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(2008) reported agreement between the teams (measured with Evalb) at 93.8% F1, the level of the CTB.
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 a given "word" in the automatic segmentation, if at least k of the hu­ man judges agree that this is a word, then that word is considered to be correct.
Their work is closely related to recent approaches that incorporate the sparsity constraint into the POS induction process.
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First, it directly encodes linguistic intuitions about POS tag assignments: the model structure reflects the one-tag-per-word property, and a type- level tag prior captures the skew on tag assignments (e.g., there are fewer unique determiners than unique nouns).
Here we present two algorithms.
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One implementation issue deserves some elaboration.
Their work is closely related to recent approaches that incorporate the sparsity constraint into the POS induction process.
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We thank members of the MIT NLP group for their suggestions and comments.
The authors show that PATB is similar to other tree-banks but that annotation consistency remains low.
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Bikel 75 training trees 5000 10000 15000 Figure 3: Dev set learning curves for sentence lengths ≤ 70.
The resulting model is compact, efficiently learnable and linguistically expressive.
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For instance, by altering the emission distribution parameters, Johnson (2007) encourages the model to put most of the probability mass on few tags.
The code is open source, has minimal dependencies, and offers both C++ and Java interfaces for integration.
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Using cn to denote the number of n-grams, total memory consumption of TRIE, in bits, is plus quantization tables, if used.
The TRIE model uses less memory than the smallest lossless alternative and is still faster than SRILM.
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This logic applies recursively: if wnf+1 similarly does not extend and has zero log backoff, it too should be omitted, terminating with a possibly empty context.
However, these approaches are ill-equipped to directly represent type-based constraints such as sparsity.
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However, our full model takes advantage of word features not present in Grac¸a et al.
The model incorporates various recent techniques for incorporating and manipulating linguistic knowledge using finite-state transducers.
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Two measures that can be used to compare judgments are: 1.
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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7 Conclusion and Future Work.
There are clustering approaches that assign a single POS tag to each word type.
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We use w erations of sampling (see Figure 2 for a depiction).
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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Semantic expectations are analogous to lexical expectations except that they represent semantic classes rather than nouns.
The experimental tests are carried out on the Verbmobil task which is a limited-domain spoken-language task.
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The cities of the traveling salesman problem correspond to source Table 1: DP algorithm for statistical machine translation.
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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In future work, we plan to follow-up on this approach and investigate other ways that contextual role knowledge can be used.
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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In this case we are interested in finding' the maximum probability parse, ri, and Mi is the set of relevant (binary) parsing decisions made by parser i. ri is a parse selected from among the outputs of the individual parsers.
This assumption, however, is not inherent to type-based tagging models.
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Evaluation Metrics We report three metrics to evaluate tagging performance.
The texts were annotated with the RSTtool.
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Consequently, we implemented our own annotation tool ConAno in Java (Stede, Heintze 2004), which provides specifically the functionality needed for our purpose.
It outperformed strong unsupervised baselines as well as approaches that relied on direct projections, and bridged the gap between purely supervised and unsupervised POS tagging models.
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We use graph-based label propagation for cross-lingual knowledge transfer and use the projected labels as features in an unsupervised model (Berg- Kirkpatrick et al., 2010).
A beam search concept is applied as in speech recognition.
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We show translation results for three approaches: the monotone search (MonS), where no word reordering is allowed (Tillmann, 1997), the quasimonotone search (QmS) as presented in this paper and the IBM style (IbmS) search as described in Section 3.2.
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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(f1; ;mg n fl1; l2; l3g ;m) German to English the monotonicity constraint is violated mainly with respect to the German verbgroup.
Throughout this paper we compare with several packages: SRILM 1.5.12 (Stolcke, 2002) is a popular toolkit based on tries used in several decoders.
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Linear probing places at most one entry in each bucket.
The TRIE model uses less memory than the smallest lossless alternative and is still faster than SRILM.
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Non-empty buckets contain an entry belonging to them or to a preceding bucket where a conflict occurred.
Here we present two algorithms.
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Note, however, that there might be situations in which Zco in fact increases.
Koehn and Monz carried out an extensive manual and automatic evaluation of machine translation performance on European language pairs.
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Annotators argued for the importance of having correct and even multiple references.
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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This vector tx is constructed for every word in the foreign vocabulary and will be used to provide features for the unsupervised foreign language POS tagger.
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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irL as the product of the probability estimate for i¥JJ1l., and the probability estimate just derived for unseen plurals in ir,: p(i¥1J1l.ir,) p(i¥1J1l.)p(unseen(f,)).
they evaluate various specific aspects of the segmentation, as well as the overall segmentation performance.
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Note that the backoff model assumes that there is a positive correlation between the frequency of a singular noun and its plural.