³ò ’B_Kc@s¤dZddkZddklZd„Zd„Zdefd„ƒYZd„Zed „Z d „Z d „Z d „Z e d joe ƒe ƒe ƒndS(sˆ Simple classifier for RTE corpus. It calculates the overlap in words and named entities between text and hypothesis, and also whether there are words / named entities in the hypothesis which fail to occur in the text, since this is an indicator that the hypothesis is more informative than (i.e not entailed by) the text. TO DO: better Named Entity classification TO DO: add lemmatization iÿÿÿÿN(taccuracycCs&|iƒp |iƒotSntS(sr This just assumes that words in all caps or titles are named entities. @type token: C{str} (tistitletisuppertTruetFalse(ttoken((s0/p/zhu/06/nlp/nltk/nltk/classify/rte_classify.pytnes  cCs4tiii|ddƒ}|tj o|Sn|S(sA Use morphy from WordNet to find the base form of verbs. tpostverb(tnltktcorpustwordnettmorphytNone(twordtlemma((s0/p/zhu/06/nlp/nltk/nltk/classify/rte_classify.pyt lemmatize#s tRTEFeatureExtractorcBs5eZdZeed„Zed„Zed„ZRS(s™ This builds a bag of words for both the text and the hypothesis after throwing away some stopwords, then calculates overlap and difference. c Cs¯||_tddddddddd d d d d ddgƒ|_tdddddgƒ|_ddkl}|dƒ}|i|iƒ|_|i|i ƒ|_ t|iƒ|_ t|i ƒ|_ |ojtg}|iD]}|||ƒqå~ƒ|_ tg}|i D]}|||ƒq~ƒ|_ n|io*|i |i|_ |i |i|_ n|i |i @|_ |i |i |_|i |i |_dS(s® @param rtepair: a L{RTEPair} from which features should be extracted @param stop: if C{True}, stopwords are thrown away. @type stop: C{bool} tatthetitttheytoftinttothavetistaretweretandtveryt.t,tnotnottnevertfailedrejectedtdeniediÿÿÿÿ(tRegexpTokenizers([A-Z]\.)+|\w+|\$[\d\.]+N(tstoptsett stopwordstnegwordst nltk.tokenizeR&ttokenizettextt text_tokensthypt hyp_tokenst text_wordst hyp_wordst_overlapt _hyp_extrat _txt_extra( tselftrtepairR'RR&t tokenizert_[1]Rt_[2]((s0/p/zhu/06/nlp/nltk/nltk/classify/rte_classify.pyt__init__1s& $ 37 cCs¯tg}|iD]}t|ƒo ||qq~ƒ}|djo|o dG|GHn|SnH|djo*|odG|i|GHn|i|Sntd|ƒ‚dS(s¸ Compute the overlap between text and hypothesis. @param toktype: distinguish Named Entities from ordinary words @type toktype: 'ne' or 'word' Rs ne overlapRs word overlapsType not recognized:'%s'N(R(R3Rt ValueError(R6ttoktypetdebugR9Rt ne_overlap((s0/p/zhu/06/nlp/nltk/nltk/classify/rte_classify.pytoverlapTs;  cCs€tg}|iD]}t|ƒo ||qq~ƒ}|djo|Sn-|djo|i|Sntd|ƒ‚dS(sº Compute the extraneous material in the hypothesis. @param toktype: distinguish Named Entities from ordinary words @type toktype: 'ne' or 'word' RRsType not recognized: '%s'N(R(R4RR<(R6R=R>R9Rtne_extra((s0/p/zhu/06/nlp/nltk/nltk/classify/rte_classify.pyt hyp_extraes ;  (t__name__t __module__t__doc__RRR;R@RB(((s0/p/zhu/06/nlp/nltk/nltk/classify/rte_classify.pyR,s# cCs¸t|ƒ}h}t|d %s(R R RPRQtsortedRO(RQRTtkey((s0/p/zhu/06/nlp/nltk/nltk/classify/rte_classify.pyt demo_features–scCs^tiiidgƒd}t|ƒ}|iGH|idƒGH|idƒGH|idƒGHdS(Ns rte3_dev.xmli!RR(R R RPRQRR2R@RB(R7RM((s0/p/zhu/06/nlp/nltk/nltk/classify/rte_classify.pytdemo_feature_extractoržs  cs‹ddk‰y ˆidƒ‡fd†}WnHtj o<y‡fd†}Wqwtj oˆii}qwXnXˆii|ƒdS(Niÿÿÿÿs/usr/local/bin/megamcsˆii|dƒS(tmegam(tMaxentClassifierRU(tx(R (s0/p/zhu/06/nlp/nltk/nltk/classify/rte_classify.pyt«scsˆii|dƒS(tBFGS(RbRU(Rc(R (s0/p/zhu/06/nlp/nltk/nltk/classify/rte_classify.pyRd®s(R t config_megamR<RbRUtclassifyR\(RS((R s0/p/zhu/06/nlp/nltk/nltk/classify/rte_classify.pytdemo§s  t__main__(RER tutilRRRtobjectRROR\R_R`RhRC(((s0/p/zhu/06/nlp/nltk/nltk/classify/rte_classify.pyss  I