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Daniel C. Mller LEXICALIZED PARSING FOR DIFFERENT DOMAINS Laura Rimell and Stephen Clark 10.12.2009 Hypothesis 2 parser adaption in the context of lexicalized grammar according to two different domains Daniel C. Mller

  1. Daniel C. Müller LEXICALIZED PARSING FOR DIFFERENT DOMAINS Laura Rimell and Stephen Clark 10.12.2009

  2. Hypothesis 2 � parser adaption in the context of lexicalized grammar � � according to two different domains Daniel C. Müller - 10.12.2009

  3. domains 3 � Biomedical domain � Questions of question answering Daniel C. Müller - 10.12.2009

  4. Lexicalized parser 4 � POS-Tagging based on Penn Tree Bank � Combinatory Categorial Grammar + manual annotation Daniel C. Müller - 10.12.2009

  5. Lexicalized parser 5 � POS-Tagging based on Penn Tree Bank � POS Tag: � 50 grammatical labels indicating part of speech � Each word one POS Tag Daniel C. Müller - 10.12.2009

  6. Lexicalized parser 6 � POS-Tagging based on Penn Tree Bank � Combinatory Categorial Grammar + manual annotation � lexical categorization (super-tagger) � 425 categories � Each word at least one category � Containing subcategorial information � Complex categories like (S\NP)/NP means: returns S\NP when applied to NP Daniel C. Müller - 10.12.2009

  7. Lexicalized parser 7 � Example � Biomedical domain POS Tag Talin|NN perhaps|RB acts|VBZ as|IN a|DT linkage|NN protein|NN .|. NP (S\NP)/(S\NP) (S[dcl]\NP)/PP PP/NP NP[nb]/N N/N N . � Question domain What|WDT king|NN signed|VBD the|DT Magna|NNP Carta|NNP ?|. (S[wq]/(S[dcl]\NP))/N N (S[dcl]\NP)/NP NP[nb]/N N/N N . lexical category Daniel C. Müller - 10.12.2009

  8. Lexicalized parser 8 � POS-Tagging based on Penn Tree Bank � Combinatory Categorial Grammar + manual annotation � lexical categorization (super-tagger) � derivation (hierarchy) � Lexicalized categories + combinatory rules Viterbi packed chart representation best derivation Daniel C. Müller - 10.12.2009

  9. Lexicalized parser 9 � Example I drink coffee NP (S\NP)/NP NP . (S\NP)/NP needs a NP to the right NP S\NP S\NP needs a NP to the left S Daniel C. Müller - 10.12.2009

  10. Motivation 10 � creating new training data at the lower levels of representation � better POS tagging better categorization � reduce annotation overhead Daniel C. Müller - 10.12.2009

  11. Experiments 11 � Training resources � Baseline � Wall Street Journal Sections 02-21 of CCGbank � Biomedical domain � POS tagger: gold-standard POS tags from GENIA � Lexical categories: � rst1,000 sentences of GENIA � parser evaluation: BioInfer � Evaluation set: Pyysalo et al. (2007b) � Question domain � Questions beginning with the word What, from the TREC 9-12 competitions: manually POS tagged & annotated with lexical categories Daniel C. Müller - 10.12.2009

  12. Experiments 12 � Results � POS-Tagger % WSJ 02-21 Retrained Sec.00 96.7 - Biomedical 93.4 98.7 Question 92.2 97.1 Daniel C. Müller - 10.12.2009

  13. Experiments 13 � Results � Supertagger % Original Retrained Retrained pipeline POS POS & Super Sec.00 91.5 - - Biomedical 89.0 91.2 93.0 Question 71.6 74.0 92.1 Daniel C. Müller - 10.12.2009

  14. Experiments 14 � Results � Parser evaluation % Original new new pipeline POS POS & Super Biomedical 76.0 80.4 81.5 Question 64.4 69.4 86.6 Daniel C. Müller - 10.12.2009

  15. Analysis 15 � Comparing to WSJ: � Biomedical domain: + similar syntactic structure � vocabulary & foreign words � long noun phrases � Question domain: + vocabulary � words with different distribution of POS in source domain � different syntactic structure Daniel C. Müller - 10.12.2009

  16. Analysis 16 � POS tagging � Biomedical domain: � nouns and adjectives (801 NN + 268 JJ errors) very long noun phrases and unknown words like � major histocompatibility complex class II molecules � � Question domain: � wh-determiners (129 errors) only one occurrence in WSJ 02-21 Daniel C. Müller - 10.12.2009

  17. Analysis 17 � POS tagging � Biomedical domain: � nouns and adjectives (801 NN + 268 JJ errors) very long noun phrases and unknown words � Question domain: � wh-determiners (129 errors) (S/(S/NP))/N: � What Liverpool club spawned the Beatles? � S/(S\NP) : � What are the colors of the German � ag? � much more errors but related syntactic structure Daniel C. Müller - 10.12.2009

  18. Analysis 18 � Syntactic differences � Unknown POS n-gram rate % WJS 02-21 New training sets 3-grams 5-grams 3-grams 5-grams Sec.00 0.4 12.1 - - Biomedical 0.7 10.9 0.5 9.2 Question 3.6 22.0 0.7 7.4 Daniel C. Müller - 10.12.2009

  19. Analysis 19 � Syntactic differences � Unknown POS n-gram rate � Number of 20 most frequent POS n-grams 3-grams 5-grams Sec.00 18 19 Biomedical 16 13 Question 8 5 Daniel C. Müller - 10.12.2009

  20. Analysis 20 � Syntactic differences � Unknown POS n-gram rate � Number of 20 most frequent POS n-grams � POS Trigrams � Biomedical domain: � Domination of NPs and PPs � Question domain: � Beginning with WP VBZ like � What is � � Ending with VB . � Daniel C. Müller - 10.12.2009

  21. Analysis 21 � Syntactic differences � Unknown POS n-gram rate � Number of 20 most frequent POS n-grams � POS Trigrams � Number of rare or unseen lexical categories Daniel C. Müller - 10.12.2009

  22. Conclusion 22 � Biomedical domain � Question domain � need for accurate parsing � long and difficult sentences � uniform sentences � many POS tag errors � less related syntax with supertagging with POS tagging parser adaption successful! Daniel C. Müller - 10.12.2009

  23. References 23 � Laura Rimell, Stephen Clark. 2008. Adapting a Lexicalized-Grammar Parser to Contrasting Domains. EMNLP 2008. � Julia Hockenmaiers. 2007. Expressive Grammar Formalisms for Natural Language: Theory and Applications. Lecture 16: Extracting a CCG from the Penn Treebank . � Julia Hockenmaiers. 2005 CCGBank User � s Manual Daniel C. Müller - 10.12.2009

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