a fast, accurate deterministic parser for chinese

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A Fast, Accurate Deterministic Parser for Chinese MengqiuWang Kenji Sagae Teruko Mitamura Language Technologies Institute School of Computer Science Carnegie Mellon University {mengqiu,sagae,teruko}@cs.cmu.edu Advisor: Hsin-Hsi Chen Speaker: Yong-Sheng Lo Date: 2007/07/26 ACL - 2006

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ACL - 2006. A Fast, Accurate Deterministic Parser for Chinese. MengqiuWang Kenji Sagae Teruko Mitamura Language Technologies Institute School of Computer Science Carnegie Mellon University {mengqiu,sagae,teruko}@cs.cmu.edu. Advisor: Hsin-Hsi Chen Speaker: Yong-Sheng Lo Date: 2007/07/26. - PowerPoint PPT Presentation

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  • A Fast, Accurate Deterministic Parser for ChineseMengqiuWang Kenji Sagae Teruko MitamuraLanguage Technologies InstituteSchool of Computer ScienceCarnegie Mellon University{mengqiu,sagae,teruko}@cs.cmu.eduAdvisor: Hsin-Hsi ChenSpeaker: Yong-Sheng LoDate: 2007/07/26ACL - 2006

  • Agenda Introduction Deterministic parsing modelThe parsing task the classification taskThe shift/reduce decisionFour classifiersFeature selectionPOS tagging Using gold-standard POS tagsA simple POS tagger using an SVM classifierExperiments ConclusionWord SegmentationPOS taggingParsing

  • Introduction Traditional statistical approaches To build models which assign probabilities to every possible parse tree for a sentenceTechniques Such as dynamic programming, beam-search, and best-first-search are then employed to find the parse tree with the highest probabilityDisadvantageToo slow for many practical applications

  • Introduction Deterministic parsing modelThe parsing task the classification taskThe shift/reduce decisionFour classifiersFeature selectionPOS tagging Using gold-standard POS tagsA simple POS tagger using an SVM classifierExperiments Conclusion

  • Deterministic Parsing ModelDeterministic parsing model :Input is a sentence has already been segmented and tagged with part-of-speech (POS) informationData structureQueue : To store the input word-POS tag pairs (ex.-NR)Stack : To hold the partial trees that are built during parsingAt each parse state The classifier makes shift/reduce decision based on contextual featuresOutput is a full parsing tree

  • Introduction Deterministic parsing modelThe parsing task the classification taskThe shift/reduce decisionFour classifiersFeature selectionPOS tagging Using gold-standard POS tagsA simple POS tagger using an SVM classifierExperiments Conclusion

  • The shift/reduce decisionFour parsing actions : (Sagae and Lavie, 2005)ShiftTo remove the first item on the queue and put it onto the stack Reduce-Unary-XTo remove one item from the stackX is the label of a new tree node that will be dominating the removed itemReduce-Binary-X-LeftTo remove two item from the stackTo take the head-node of the left sub-tree to be the head of the new treeReduce-Binary-X-RightTo remove two item from the stackTo take the head-node of the right sub-tree to be the head of the new tree

  • For example 1/5Input : (NR) (VV) (NR)

    The parse state : Initialization

    Action : Shift

  • For example 2/5The parse state : 2

    Action : Reduce-Unary-NP

    The parse state : 3

    Action : Shift

  • For example 3/5The parse state : 4

    Action : Shift

    The parse state : 5

    Action : Reduce-Unary-NP

  • For example 4/5The parse state : 6

    Action : Reduce-Binary-VP-LeftThe parse state : 7

    Action : Reduce-Binary-IP-Right

  • For example 5/5The parse state : final

  • Introduction Deterministic parsing modelThe parsing task the classification taskThe shift/reduce decisionFour classifiersFeature selectionPOS tagging Using gold-standard POS tagsA simple POS tagger using an SVM classifierExperiments Conclusion

  • Four classifiers Support Vector Machine The TinySVM toolkit (Kudo andMatsumoto,2000)Maximum-Entropy ClassifierThe Les Maxent toolkit (Zhang, 2004)Decision Tree ClassifierThe C4.5 decision tree classifierMemory-Based LearningThe TiMBL toolkit (Daelemans et al., 2004)

  • Introduction Deterministic parsing modelThe parsing task the classification taskThe shift/reduce decisionFour classifiersFeature selectionPOS tagging Using gold-standard POS tagsA simple POS tagger using an SVM classifierExperiments Conclusion

  • Feature selection

  • Introduction Deterministic parsing modelThe parsing task the classification taskThe shift/reduce decisionFour classifiersFeature selectionPOS tagging Using gold-standard POS tagsA simple POS tagger using an SVM classifierExperiments Conclusion

  • POS taggingUsing gold-standard POS tagsA simple POS tagger using an SVM classifierUsing gold-standard POS tags to train SVMUsing a simple POS taggerTwo passesPass 1 : To extract features from the two words and POS tags that came before the current word, the two words following the current word, and the current word itselfThen the tag is assigned to the word according to SVM classifiers outputPass 2 : Additional features such as the POS tags of the two words following the current word, and the POS tag of the current word (assigned in the first pass) are usedThis tagger had a measured precision of 92.5% for sentences
  • Experiments Corpus Penn Chinese TreebankTraining : Sections 001-270 (3484 sentences, 84,873 words) Development : 271-300 (348 sentences, 7980 words)Testing : 271-300 (348 sentences, 7980 words)99629 wordsEvaluation Labeled recall (LR)Labeled precision (LP)F1 score (harmonic mean of LR and LP)

  • Experiments

    Results of different classifiersOn development set for sentence

  • Experiments Comparison with Related workOn the test set

  • Experiments Using gold-standard POS

    Stacked classifier modelUsing Maxent, DTree and TiNBLoutputs as features, in addition to the original feature set, to train a new SVM model on the original training set

  • ConclusionTo present a novel classifier-based deterministic parser for Chinese constituency parsingThe best model runs in linear time and has labeled precision and recall above 88% using gold-standard part-of-speech tagsThe SVM parser is 2-13 times faster than state-of-the-art parsers, while producing more accurate resultsThe Maxent and DTree parsers run at speeds 40-270 times faster than state-of-the-art parsers, but with 5-6% losses in accuracy

    Maxent ,