... organizing them into semantic classes. For example, {red, white, black…} is a semanticclass consisting of color instances. A popular way forsemanticclass dis-covery is pattern- based approach, ... related to multiple topics in some topicmodels (e.g., pLSI and LDA). Topic modeling Semantic class construction word item (word or phrase) document RASC topic semantic class Table 3. ... 459–467,Suntec, Singapore, 2-7 August 2009.c2009 ACL and AFNLP Employing TopicModelsfor Pattern- basedSemanticClassDiscovery Huibin Zhang1* Mingjie Zhu2* Shuming Shi3 Ji-Rong Wen3...
... relevant transla-tions based on topic- specific contexts, wheretopics are induced in an unsupervised wayusing topic models; this can be thought ofas inducing subcorpora for adaptation with-out ... probability under topic 1, topic 2,etc., or F2: What is the probability under the mostprobable topic, second most, etc.A model using F1learns whether a specific topic is useful for translation, ... utilize informa-tion from any topic distribution if it helps us cre-ate topic relevant translations. F2is useful for dy-namic adaptation, where the adapted feature weightchanges based on...
... Association for Computational Linguistics, pages 620–631,Portland, Oregon, June 19-24, 2011.c2011 Association for Computational LinguisticsIncremental Syntactic Language Modelsfor Phrase -based ... aretherefore a good mechanism for incorporat-ing syntax into phrase -based translation. Wegive a formal definition of one such linear-time syntactic language model, detail its re-lation to phrase -based ... has effectively used n-gram word sequence models as language models. Modern phrase -based translation using large scalen-gram language models generally performs wellin terms of lexical choice,...
... in-formation indicates that the k words are more likely to form a semanticpattern of length k. Here the length k also ranges from 2 to 4. For each k, we compute the mutual information for ... each of the better semantic patterns. After the initial set for a particular length is created, each semanticpattern and the seed pattern are represented in the HAL space for further computing ... +1Cascaded Induction ProcessInducedPatternsRelevant Patterns Figure 1. Framework for variable-length seman-tic pattern induction. semantically related to the seed pattern. According to the distance...
... encode some form of semantic knowledge.The semantic similarity and relatedness of wordshas traditionally been assessed through corpus- based and knowledge -based measures. Corpus- based measures ... determined empirically based on MAP and R-Prec).The performance gaps between the translation -based models and the baseline models are statisticallysignificant, except for those marked with a ... terms “mosquito” (for question 2) and “form” (for question 3), but only their inflected forms“mosquitoes” and “formed”.6 Conclusion and Future WorkWe have presented three datasets for training sta-tistical...
... the final classi-fiers consisted of one million training instances for the start classifier, 500,000 for the end classifier,and 272,000 for the role classifier. The featuresused by the classifiers ... assume that the frame was knowna priori. We used the available semantic roles for all senses of the target word as features for theclassifier.On a test set from FrameNet, we estimated thatthe ... SRL system for Swedish text. Like mostprevious systems, it consists of two parts: a FEbracketer and a classifier that assigns semantic roles to FEs. Both parts are implemented as SVMclassifiers...
... framework, real-time is re-quired.4 Phrase -based models The usual statistical translation models can beclassified as single-word based alignment models. Models of this kind assume that an input ... Association for Computational LinguisticsStatistical phrase -based modelsfor interactive computer-assistedtranslationJes´us Tom´as and Francisco CasacubertaInstituto Tecnol´ogico de Inform´aticaUniversidad ... framework (Och et al., 2003).Phrase -based models have proved to be very ad-equate statistical modelsfor MT (Tom´as et al.,2005). In this work, the use of these models hasbeen extended to interactive...
... describe a technique for estimat- ing the parameters for this model using decision trees. The history -based grammar model provides a mechanism for taking advantage of contextual information from ... contextual information used by their mod- els is because of the difficulty in estimating very rich probabilistic models of context. In this work, we present a model, the history -based grammar ... h Grammar and parse tree for aabb. dexed the non-terminal (NT) nodes of the tree with this leftmost order. We denote by ~- the sen- tential form obtained just before we expand node i. Hence,...
... (Zitouni et al., 2003) for languagemodeling and by (Gildea, 2001) forsemantic rolelabeling. (Resnik et al., 2001) used backoff trans-lation lexicons for cross-language information re-trieval. ... re-trieval. More recently, (Xi and Hwa, 2005) haveused backoff modelsfor combining in-domain and42Phrase -Based Backoff Modelsfor Machine Translation of Highly InflectedLanguagesMei YangDepartment ... accordingly. The phrase table willthus include entries for phrases based on full wordforms as well as for their stemmed and/or splitcounterparts. For each entry with decomposed morphologicalii...
... developed for automatic semanticclass identification, under therubrics of lexical acquisition, hyponym acquisition, semantic lexicon induction, semanticclass learn-ing, and web -based information ... justonedoubly-anchored pattern to identify candidateinstances for a semantic class: < ;class name> such as < ;class member> and *This pattern has two variables: the name of the se-mantic class to ... both the name of the semantic class as well as a class member. For example, the pattern “CARS such as FORDand *” will extract automobiles, and the pattern “PRESIDENTS such as FORD and *” will extractpresidents....
... Table 2. Performance results for different models Table 2 details the performance scores for the experiments, which shows that both of the two distributional similarity -based models boost ... methods for error model estimation are all based on the similarity between the character strings of qi and ci as described in 3.1. Here we describe a distributional similarity- based method for ... Similarity -Based Mod-els for Query Spelling Correction 3.1 Motivation Most of the previous work on spelling correction concentrates on the problem of designing better error modelsbased on...
... pairs for the input term by pattern- based and distribu-tional similarity methods (Section 3.2); 2: Constructing a feature set for all candidates based on pattern- based and distributional in-formation ... features, suitable for supervised classification. To this end we developed a novel feature set based on both pattern- based and distributional data. To obtain pattern statistics for each pair, ... found in such patterns. In addition to their use for learning lexi-cal semantic relations, patterns were commonly used to learn instances of concrete semantic rela-tions for Information Extraction...
... of a topic. Given a specific topic- weightvector θd for a document-pair, each sentence-pairdraws its conditionally independent topics from amixture of topics. This generative process, for adocument-pair ... on topical translation models concern mainly explicit logical representations ofsemantics for machine translation. This includeknowledge -based (Nyberg and Mitamura, 1992)and interlingua -based ... success. Wepropose a new statistical formalism: Bilingual Topic AdMixture model, or BiTAM, to facilitate topic- based word alignment in SMT.Variants of admixture models have appeared inpopulation...
... long-term information.In this paper the best performing measuresfrom (Pucher, 2005), which outperform baseline models on word prediction for conversational tele-phone speech are used for Automatic ... dependencies. Therefore (Bellegarda,2000) proposed combining n-gram language mod-els, which are effective for predicting local de-pendencies, with Latent Semantic Analysis (LSA) based modelsfor covering ... WordNet -based models can beused for rescoring of ‘real’ N-best lists in a difficulttask.1.1 Word prediction by semantic similarityThe standard n-gram approach in language mod-eling for speech...
... information is used, it is unclear what semantic information is derived by the rules themselves, and what seman- tic information is available because of unifications with the original semantics. ... category. For example, here is the rule for sen- tences: s(Form, GO-G, Store)/quant(Q,X,R,S) > s(Form, GO-G, [qterm(Q,X,R) JStore])/S. The term quant (C~, X, R, S) represents a quantified formula ... vp(nonfinite,[np(_)/you])/S. s(Form)/S > Subj, vp(Fona,[Subj])/S. (2) vp(Form,Subcat)/S > vp(Form,[Compl[Subcat])/S, Compl. (3) vp(Form,[Subj])/S > vp(Forl,[Subj])/VP, adv(VP)/S. vp(finite,[np(_)/O,np(3-sing)/S])/love(S,O)...