B_Kc+@sddkZddkZddklZlZddklZddklZddk l Z ddk l Z dZdd d d d f\ZZZZZeeeegZed ddddddddddddddddddd d!d"d#d$d%d&d'd(d)d*d+d,d-d.d/f$Zd0efd1YZd2efd3YZd4efd5YZd6efd7YZd8efd9YZd:efd;YZed<Z ei i!e _!ed=Z"ei"i!e"_!ed>Z#ei#i!e#_!ed?Z$ei$i!e$_!ed@Z%ei%i!e%_!edAZ&ei&i!e&_!edBZ'edCZ(dDZ)dEZ*dFZ+e,dGjo e+ndS(HiN(tislicetchain(t defaultdict(t CorpusReader(tbinary_search_file(tFreqDistgu<7~tatstrtntvs Something %ss Somebody %ss It is %singsSomething is %sing PPs%Something %s something Adjective/NounsSomething %s Adjective/NounsSomebody %s AdjectivesSomebody %s somethingsSomebody %s somebodysSomething %s somebodysSomething %s somethingsSomething %s to somebodysSomebody %s on somethingsSomebody %s somebody somethings!Somebody %s something to somebodys#Somebody %s something from somebodys#Somebody %s somebody with somethings!Somebody %s somebody of somethings!Somebody %s something on somebodysSomebody %s somebody PPsSomebody %s something PPsSomebody %s PPsSomebody's (body part) %ss"Somebody %s somebody to INFINITIVEsSomebody %s somebody INFINITIVEsSomebody %s that CLAUSEsSomebody %s to somebodysSomebody %s to INFINITIVEsSomebody %s whether INFINITIVEs)Somebody %s somebody into V-ing somethings$Somebody %s something with somethingsSomebody %s INFINITIVEsSomebody %s VERB-ingsIt %s that CLAUSEsSomething %s INFINITIVEt WordNetErrorcBseZdZRS(s.An exception class for wordnet-related errors.(t__name__t __module__t__doc__(((s0/p/zhu/06/nlp/nltk/nltk/corpus/reader/wordnet.pyR Xst_WordNetObjectcBseZdZdZdZdZdZdZdZdZ dZ d Z d Z d Z d Zd ZdZdZdZdZdZdZRS(s+A common base class for lemmas and synsets.cCs |idS(Nt@(t_related(tself((s0/p/zhu/06/nlp/nltk/nltk/corpus/reader/wordnet.pyt hypernyms_scCs |idS(Ns@i(R(R((s0/p/zhu/06/nlp/nltk/nltk/corpus/reader/wordnet.pytinstance_hypernymsbscCs |idS(Nt~(R(R((s0/p/zhu/06/nlp/nltk/nltk/corpus/reader/wordnet.pythyponymsescCs |idS(Ns~i(R(R((s0/p/zhu/06/nlp/nltk/nltk/corpus/reader/wordnet.pytinstance_hyponymshscCs |idS(Ns#m(R(R((s0/p/zhu/06/nlp/nltk/nltk/corpus/reader/wordnet.pytmember_holonymskscCs |idS(Ns#s(R(R((s0/p/zhu/06/nlp/nltk/nltk/corpus/reader/wordnet.pytsubstance_holonymsnscCs |idS(Ns#p(R(R((s0/p/zhu/06/nlp/nltk/nltk/corpus/reader/wordnet.pyt part_holonymsqscCs |idS(Ns%m(R(R((s0/p/zhu/06/nlp/nltk/nltk/corpus/reader/wordnet.pytmember_meronymstscCs |idS(Ns%s(R(R((s0/p/zhu/06/nlp/nltk/nltk/corpus/reader/wordnet.pytsubstance_meronymswscCs |idS(Ns%p(R(R((s0/p/zhu/06/nlp/nltk/nltk/corpus/reader/wordnet.pyt part_meronymszscCs |idS(Nt=(R(R((s0/p/zhu/06/nlp/nltk/nltk/corpus/reader/wordnet.pyt attributes}scCs |idS(Nt*(R(R((s0/p/zhu/06/nlp/nltk/nltk/corpus/reader/wordnet.pyt entailmentsscCs |idS(Nt>(R(R((s0/p/zhu/06/nlp/nltk/nltk/corpus/reader/wordnet.pytcausesscCs |idS(Nt^(R(R((s0/p/zhu/06/nlp/nltk/nltk/corpus/reader/wordnet.pyt also_seesscCs |idS(Nt$(R(R((s0/p/zhu/06/nlp/nltk/nltk/corpus/reader/wordnet.pyt verb_groupsscCs |idS(Nt&(R(R((s0/p/zhu/06/nlp/nltk/nltk/corpus/reader/wordnet.pyt similar_tosscCs t|iS(N(thashtname(R((s0/p/zhu/06/nlp/nltk/nltk/corpus/reader/wordnet.pyt__hash__scCs|i|ijS(N(R+(Rtother((s0/p/zhu/06/nlp/nltk/nltk/corpus/reader/wordnet.pyt__eq__scCs|i|ijS(N(R+(RR-((s0/p/zhu/06/nlp/nltk/nltk/corpus/reader/wordnet.pyt__ne__s(R R RRRRRRRRRRRRR!R#R%R'R)R,R.R/(((s0/p/zhu/06/nlp/nltk/nltk/corpus/reader/wordnet.pyR\s(                  tLemmacBsMeZdZdZdZdZdZdZdZdZ RS(sD The lexical entry for a single morphological form of a sense-disambiguated word. Create a Lemma from a "..." string where: is the morphological stem identifying the synset is one of the module attributes ADJ, ADJ_SAT, ADV, NOUN or VERB is the sense number, counting from 0. is the morphological form of interest Note that and can be different, e.g. the Synset 'salt.n.03' has the Lemmas 'salt.n.03.salt', 'salt.n.03.saltiness' and 'salt.n.03.salinity'. Lemma attributes ---------------- name - The canonical name of this lemma. synset - The synset that this lemma belongs to. syntactic_marker - For adjectives, the WordNet string identifying the syntactic position relative modified noun. See: http://wordnet.princeton.edu/man/wninput.5WN.html#sect10 For all other parts of speech, this attribute is None. Lemma methods ------------- Lemmas have the following methods for retrieving related Lemmas. They correspond to the names for the pointer symbols defined here: http://wordnet.princeton.edu/man/wninput.5WN.html#sect3 These methods all return lists of Lemmas. antonyms hypernyms instance_hypernyms hyponyms instance_hyponyms member_holonyms substance_holonyms part_holonyms member_meronyms substance_meronyms part_meronyms attributes derivationally_related_forms entailments causes also_sees verb_groups similar_tos pertainyms cCsU||_||_||_||_g|_g|_||_||_d|_ dS(N( t_wordnet_corpus_readerR+tsyntactic_markertsynsett frame_stringst frame_idst_lexname_indext_lex_idtNonetkey(Rtwordnet_corpus_readerR3R+t lexname_indextlex_idR2((s0/p/zhu/06/nlp/nltk/nltk/corpus/reader/wordnet.pyt__init__s        cCs)t|i|ii|if}d|S(Ns %s('%s.%s')(ttypeR R3R+(Rttup((s0/p/zhu/06/nlp/nltk/nltk/corpus/reader/wordnet.pyt__repr__s!cCsW|ii}g}|ii|i|fD]&\}}}||||i|q*~S(N(R1t_synset_from_pos_and_offsetR3t_lemma_pointersR+tlemmas(Rtrelation_symbolt get_synsett_[1]tpostoffsett lemma_index((s0/p/zhu/06/nlp/nltk/nltk/corpus/reader/wordnet.pyRs cCs|ii|S(s)Return the frequency count for this Lemma(R1t lemma_count(R((s0/p/zhu/06/nlp/nltk/nltk/corpus/reader/wordnet.pytcountscCs |idS(Nt!(R(R((s0/p/zhu/06/nlp/nltk/nltk/corpus/reader/wordnet.pytantonymsscCs |idS(Nt+(R(R((s0/p/zhu/06/nlp/nltk/nltk/corpus/reader/wordnet.pytderivationally_related_formsscCs |idS(Ns\(R(R((s0/p/zhu/06/nlp/nltk/nltk/corpus/reader/wordnet.pyt pertainymss( R R RR=R@RRKRMRORP(((s0/p/zhu/06/nlp/nltk/nltk/corpus/reader/wordnet.pyR0s2     tSynsetcBseZdZdZdZdZdZddZdZdZ d Z d d Z d Z de d ZedZedZedZedZedZedZdZdZdZRS(s]Create a Synset from a ".." string where: is the word's morphological stem is one of the module attributes ADJ, ADJ_SAT, ADV, NOUN or VERB is the sense number, counting from 0. Synset attributes ----------------- name - The canonical name of this synset, formed using the first lemma of this synset. Note that this may be different from the name passed to the constructor if that string used a different lemma to identify the synset. pos - The synset's part of speech, matching one of the module level attributes ADJ, ADJ_SAT, ADV, NOUN or VERB. lemmas - A list of the Lemma objects for this synset. definition - The definition for this synset. examples - A list of example strings for this synset. offset - The offset in the WordNet dict file of this synset. #lexname - The name of the lexicographer file containing this synset. Synset methods -------------- Synsets have the following methods for retrieving related Synsets. They correspond to the names for the pointer symbols defined here: http://wordnet.princeton.edu/man/wninput.5WN.html#sect3 These methods all return lists of Synsets. hypernyms instance_hypernyms hyponyms instance_hyponyms member_holonyms substance_holonyms part_holonyms member_meronyms substance_meronyms part_meronyms attributes entailments causes also_sees verb_groups similar_tos Additionally, Synsets support the following methods specific to the hypernym relation: root_hypernyms common_hypernyms lowest_common_hypernyms Note that Synsets do not support the following relations because these are defined by WordNet as lexical relations: antonyms derivationally_related_forms pertainyms cCs||_d|_d|_d|_g|_g|_g|_g|_d|_ g|_ d|_ t t |_t t |_dS(N(R1R8RGRHR+R5RCt lemma_namest lemma_infost definitiontexamplestlexnameRtsett _pointersRB(RR:((s0/p/zhu/06/nlp/nltk/nltk/corpus/reader/wordnet.pyR=.s           cCsg}t}|g}xq|oi|i}||joL|i||i|i}|p|i|q|i|qqW|S(s4Get the topmost hypernyms of this synset in WordNet.(RWtpoptaddRRtappendtextend(Rtresulttseenttodot next_synsettnext_hypernyms((s0/p/zhu/06/nlp/nltk/nltk/corpus/reader/wordnet.pytroot_hypernymsAs        cCsbd|ijoK|i|i}|p d|_q[dtd|D|_n|iS(sh @return: The length of the longest hypernym path from this synset to the root. t _max_depthiicssx|]}|iVqWdS(N(t max_depth(t.0th((s0/p/zhu/06/nlp/nltk/nltk/corpus/reader/wordnet.pys fs(t__dict__RRRctmax(RR((s0/p/zhu/06/nlp/nltk/nltk/corpus/reader/wordnet.pyRd[s  !cCsbd|ijoK|i|i}|p d|_q[dtd|D|_n|iS(si @return: The length of the shortest hypernym path from this synset to the root. t _min_depthiicssx|]}|iVqWdS(N(t min_depth(ReRf((s0/p/zhu/06/nlp/nltk/nltk/corpus/reader/wordnet.pys ts(RgRRRitmin(RR((s0/p/zhu/06/nlp/nltk/nltk/corpus/reader/wordnet.pyRjis  !iccswddkl}g}xZ||||D]F}|i|ijo-|i|jo|i|i|Vqoq)q)WdS(sReturn the transitive closure of source under the rel relationship, breadth-first >>> from nltk.corpus import wordnet as wn >>> dog = wn.synset('dog.n.01') >>> hyp = lambda s:s.hypernyms() >>> list(dog.closure(hyp)) [Synset('domestic_animal.n.01'), Synset('canine.n.02'), Synset('animal.n.01'), Synset('carnivore.n.01'), Synset('organism.n.01'), Synset('placental.n.01'), Synset('living_thing.n.01'), Synset('mammal.n.01'), Synset('whole.n.02'), Synset('vertebrate.n.01'), Synset('object.n.01'), Synset('chordate.n.01'), Synset('physical_entity.n.01'), Synset('entity.n.01')] i(t breadth_firstN(t nltk.utilRlRHR[(RtreltdepthRltsynset_offsetsR3((s0/p/zhu/06/nlp/nltk/nltk/corpus/reader/wordnet.pytclosurewscCs{g}|i}t|djo|gg}nx?|D]7}x.|iD] }|i||i|qOWq<W|S(s$ Get the path(s) from this synset to the root, where each path is a list of the synset nodes traversed on the way to the root. @return: A list of lists, where each list gives the node sequence connecting the initial L{Synset} node and a root node. i(Rtlenthypernym_pathsR[(RtpathsRthypernymt ancestor_list((s0/p/zhu/06/nlp/nltk/nltk/corpus/reader/wordnet.pyRss   cCsKtd|iD}td|iD}t|i|S(s Find all synsets that are hypernyms of this synset and the other synset. @type other: L{Synset} @param other: other input synset. @return: The synsets that are hypernyms of both synsets. css*x#|]}x|D] }|VqWqWdS(N((Ret self_synsetst self_synset((s0/p/zhu/06/nlp/nltk/nltk/corpus/reader/wordnet.pys s css*x#|]}x|D] }|VqWqWdS(N((Ret other_synsetst other_synset((s0/p/zhu/06/nlp/nltk/nltk/corpus/reader/wordnet.pys s (RWt_iter_hypernym_liststlistt intersection(RR-RwRy((s0/p/zhu/06/nlp/nltk/nltk/corpus/reader/wordnet.pytcommon_hypernymss  c Cs|i}|i}td|D}td|D}|i|yPtd|D}g}|D]$}|i|jo ||ququ~SWntj o gSnXdS(s;Get the lowest synset that both synsets have as a hypernym.css*x#|]}x|D] }|VqWqWdS(N((RetsynsetsR((s0/p/zhu/06/nlp/nltk/nltk/corpus/reader/wordnet.pys scss*x#|]}x|D] }|VqWqWdS(N((ReRR((s0/p/zhu/06/nlp/nltk/nltk/corpus/reader/wordnet.pys scssx|]}|iVqWdS(N(Rj(ReR((s0/p/zhu/06/nlp/nltk/nltk/corpus/reader/wordnet.pys sN(R{RWtintersection_updateRhRjt ValueError( RR-tself_hypernymstother_hypernymsRtothersRdRFR((s0/p/zhu/06/nlp/nltk/nltk/corpus/reader/wordnet.pytlowest_common_hypernymss   :icCsQt||fg}x5|i|iD]}||i|dO}q,W|S(s Get the path(s) from this synset to the root, counting the distance of each node from the initial node on the way. A set of (synset, distance) tuples is returned. @type distance: C{int} @param distance: the distance (number of edges) from this hypernym to the original hypernym L{Synset} on which this method was called. @return: A set of (L{Synset}, int) tuples where each L{Synset} is a hypernym of the first L{Synset}. i(RWRRthypernym_distances(Rtdistancet distancesRu((s0/p/zhu/06/nlp/nltk/nltk/corpus/reader/wordnet.pyRs cCs2||jodSnt}|i}h}|i}h}xw||f||fgD]]\}}xN|D]F\} } | |jo#| || jo| || >> from nltk.corpus import wordnet as wn >>> dog = wn.synset('dog.n.01') >>> hyp = lambda s:s.hypernyms() >>> from pprint import pprint >>> pprint(dog.tree(hyp)) [Synset('dog.n.01'), [Synset('domestic_animal.n.01'), [Synset('animal.n.01'), [Synset('organism.n.01'), [Synset('living_thing.n.01'), [Synset('whole.n.02'), [Synset('object.n.01'), [Synset('physical_entity.n.01'), [Synset('entity.n.01')]]]]]]]], [Synset('canine.n.02'), [Synset('carnivore.n.01'), [Synset('placental.n.01'), [Synset('mammal.n.01'), [Synset('vertebrate.n.01'), [Synset('chordate.n.01'), [Synset('animal.n.01'), [Synset('organism.n.01'), [Synset('living_thing.n.01'), [Synset('whole.n.02'), [Synset('object.n.01'), [Synset('physical_entity.n.01'), [Synset('entity.n.01')]]]]]]]]]]]]]] ii(ttree(RRnRotcut_markRRFtx((s0/p/zhu/06/nlp/nltk/nltk/corpus/reader/wordnet.pyRs   BcCs4|i|}|djod|dSndSdS(sf Path Distance Similarity: Return a score denoting how similar two word senses are, based on the shortest path that connects the senses in the is-a (hypernym/hypnoym) taxonomy. The score is in the range 0 to 1, except in those cases where a path cannot be found (will only be true for verbs as there are many distinct verb taxonomies), in which case None is returned. A score of 1 represents identity i.e. comparing a sense with itself will return 1. @type other: L{Synset} @param other: The L{Synset} that this L{Synset} is being compared to. @return: A score denoting the similarity of the two L{Synset}s, normally between 0 and 1. None is returned if no connecting path could be found. 1 is returned if a L{Synset} is compared with itself. ig?iN(RR8(RR-tverboseR((s0/p/zhu/06/nlp/nltk/nltk/corpus/reader/wordnet.pytpath_similarity.s cCs|i|ijotdd||fn|i|iijo|ii|in|ii|i}|i|}|djoti|dd| SndSdS(s% Leacock Chodorow Similarity: Return a score denoting how similar two word senses are, based on the shortest path that connects the senses (as above) and the maximum depth of the taxonomy in which the senses occur. The relationship is given as -log(p/2d) where p is the shortest path length and d is the taxonomy depth. @type other: L{Synset} @param other: The L{Synset} that this L{Synset} is being compared to. @return: A score denoting the similarity of the two L{Synset}s, normally greater than 0. None is returned if no connecting path could be found. If a L{Synset} is compared with itself, the maximum score is returned, which varies depending on the taxonomy depth. s&Computing the lch similarity requires s*%s and %s to have the same part of speech.iig@N( RGR R1Rct_compute_max_depthRtmathtlogR8(RR-RRoR((s0/p/zhu/06/nlp/nltk/nltk/corpus/reader/wordnet.pytlch_similarityGs cCs|i|}t|djotSn|d}|id}|itjo|d7}n|i|}|i|}|tjp |tjotSn||7}||7}d|||S(s Wu-Palmer Similarity: Return a score denoting how similar two word senses are, based on the depth of the two senses in the taxonomy and that of their Least Common Subsumer (most specific ancestor node). Note that at this time the scores given do _not_ always agree with those given by Pedersen's Perl implementation of WordNet Similarity. The LCS does not necessarily feature in the shortest path connecting the two senses, as it is by definition the common ancestor deepest in the taxonomy, not closest to the two senses. Typically, however, it will so feature. Where multiple candidates for the LCS exist, that whose shortest path to the root node is the longest will be selected. Where the LCS has multiple paths to the root, the longer path is used for the purposes of the calculation. @type other: L{Synset} @param other: The L{Synset} that this L{Synset} is being compared to. @return: A float score denoting the similarity of the two L{Synset}s, normally greater than zero. If no connecting path between the two senses can be found, None is returned. iig@(RRrR8RdRGtNOUNR(RR-Rt subsumerstsubsumerRotlen1tlen2((s0/p/zhu/06/nlp/nltk/nltk/corpus/reader/wordnet.pytwup_similarityjs   cCst|||\}}}|S(s Resnik Similarity: Return a score denoting how similar two word senses are, based on the Information Content (IC) of the Least Common Subsumer (most specific ancestor node). @type other: L{Synset} @param other: The L{Synset} that this L{Synset} is being compared to. @type ic: C{dict} @param ic: an information content object (as returned by L{load_ic()}). @return: A float score denoting the similarity of the two L{Synset}s. Synsets whose LCS is the root node of the taxonomy will have a score of 0 (e.g. N['dog'][0] and N['table'][0]). (t_lcs_ic(RR-ticRtic1tic2tlcs_ic((s0/p/zhu/06/nlp/nltk/nltk/corpus/reader/wordnet.pytres_similarityscCs||jotSnt|||\}}}|djp |djodSn||d|}|djotSnd|S(s Jiang-Conrath Similarity: Return a score denoting how similar two word senses are, based on the Information Content (IC) of the Least Common Subsumer (most specific ancestor node) and that of the two input Synsets. The relationship is given by the equation 1 / (IC(s1) + IC(s2) - 2 * IC(lcs)). @type other: L{Synset} @param other: The L{Synset} that this L{Synset} is being compared to. @type ic: C{dict} @param ic: an information content object (as returned by L{load_ic()}). @return: A float score denoting the similarity of the two L{Synset}s. iii(t_INFR(RR-RRRRRt ic_difference((s0/p/zhu/06/nlp/nltk/nltk/corpus/reader/wordnet.pytjcn_similaritys  cCs+t|||\}}}d|||S(s Lin Similarity: Return a score denoting how similar two word senses are, based on the Information Content (IC) of the Least Common Subsumer (most specific ancestor node) and that of the two input Synsets. The relationship is given by the equation 2 * IC(lcs) / (IC(s1) + IC(s2)). @type other: L{Synset} @param other: The L{Synset} that this L{Synset} is being compared to. @type ic: C{dict} @param ic: an information content object (as returned by L{load_ic()}). @return: A float score denoting the similarity of the two L{Synset}s, in the range 0 to 1. g@(R(RR-RRRRR((s0/p/zhu/06/nlp/nltk/nltk/corpus/reader/wordnet.pytlin_similaritysccs|g}t}x~|ovx|D]}|i|q#W|Vg}|D];}|i|iD]}||jo ||qdqdqJ~}qWdS(s @return: An iterator over L{Synset}s that are either proper hypernyms or instance of hypernyms of the synset. N(RWRZRR(RR_R^R3RFRu((s0/p/zhu/06/nlp/nltk/nltk/corpus/reader/wordnet.pyR{s     cCsdt|i|ifS(Ns%s(%r)(R>R R+(R((s0/p/zhu/06/nlp/nltk/nltk/corpus/reader/wordnet.pyR@scCsG|ii}|i|}g}|D]\}}||||q$~S(N(R1RARX(RRDREtpointer_tuplesRFRGRH((s0/p/zhu/06/nlp/nltk/nltk/corpus/reader/wordnet.pyRs  (R R RR=RbRdRjRqRsR~RRRR8RtFalseRRRRRRR{R@R(((s0/p/zhu/06/nlp/nltk/nltk/corpus/reader/wordnet.pyRQs*9          3&  # 4    tWordNetCorpusReadercBsveZdZeZdO\ZZZZZ hde<de<de<d e ss cntlist.revtlexnamess index.senses index.adjs index.advs index.nouns index.verbsdata.adjsdata.advs data.nouns data.verbsadj.excsadv.excsnoun.excsverb.exccCsti|||id|itt|_tt|_tt|_h|_ h|_ g|_ d|_ d|_xbt|idD]K\}}|i\}}}t||jpt|i i|qW|i|idS(s_ Construct a new wordnet corpus reader, with the given root directory. tencodingRN(RR=t_FILESt _ENCODINGRtdictt_lemma_pos_offset_mapt_synset_offset_cacheRct_data_file_mapt_exception_mapt _lexnamesR8t_key_count_filet_key_synset_filet enumeratetopentsplittinttAssertionErrorR[t_load_lemma_pos_offset_mapt_load_exception_map(RtroottitlinetindexRVt_((s0/p/zhu/06/nlp/nltk/nltk/corpus/reader/wordnet.pyR=s"        c Csx|iiD]}xt|id|D]\}}|idoq0nt|ii}y|}|}t|}|djpt t|}g} t |D]} | |q~ } t|} || jpt t|} g} t |D]} | t|q~ } WnBt t fj o0}d||d|f}t d|nX| |i ||<|tjo| |i |ts N(R8RR(RRG((RRGs0/p/zhu/06/nlp/nltk/nltk/corpus/reader/wordnet.pytall_lemma_nameszs c cso|djo|ii}n |g}|i}|i}x-|D]%}|tjo t}nd|i|}|i|}y|i}|i } x| o| di ps|||jo|||} n||| } | |||<|tjo| i |jo | Vq$q(| Vn|i}|i } qWWn|i qBX|i qBWdS(sIterate over all synsets with a given part of speech tag. If no pos is specified, all synsets for all parts of speech will be loaded. sdata.%siN( R8RRRRRRRttellRtisspaceRGtclose( RRGtpos_tagstcachetfrom_pos_and_linetpos_tagRRRHRR3((s0/p/zhu/06/nlp/nltk/nltk/corpus/reader/wordnet.pyRs<              cCsh|idjo|id|_nt|i|i}|ot|idddSndSdS(s)Return the frequency count for this Lemmas cntlist.revRiiN(RR8RRR9RR(RRR((s0/p/zhu/06/nlp/nltk/nltk/corpus/reader/wordnet.pyRJs cCs|i||S(N(R(RRRR((s0/p/zhu/06/nlp/nltk/nltk/corpus/reader/wordnet.pyRscCs|i||S(N(R(RRRR((s0/p/zhu/06/nlp/nltk/nltk/corpus/reader/wordnet.pyRscCs|i||S(N(R(RRRR((s0/p/zhu/06/nlp/nltk/nltk/corpus/reader/wordnet.pyRscCs|i|||S(N(R(RRRRR((s0/p/zhu/06/nlp/nltk/nltk/corpus/reader/wordnet.pyRscCs|i|||S(N(R(RRRRR((s0/p/zhu/06/nlp/nltk/nltk/corpus/reader/wordnet.pyRscCs|i|||S(N(R(RRRRR((s0/p/zhu/06/nlp/nltk/nltk/corpus/reader/wordnet.pyRscs|djo,|itfdtD}n|i|}tt|d}t|djo |dSndSdS(s Find a possible base form for the given form, with the given part of speech, by checking WordNet's list of exceptional forms, and by recursively stripping affixes for this part of speech until a form in WordNet is found. >>> from nltk.corpus import wordnet as wn >>> wn.morphy('dogs') 'dog' >>> wn.morphy('churches') 'church' >>> wn.morphy('aardwolves') 'aardwolf' >>> wn.morphy('abaci') 'abacus' >>> wn.morphy('hardrock', ADV) >>> wn.morphy('book', wn.NOUN) 'book' >>> wn.morphy('book', wn.ADJ) c3s3x,|]%}x|D] }|VqWqWdS(N((ReR%R(tformR$(s0/p/zhu/06/nlp/nltk/nltk/corpus/reader/wordnet.pys siiN(R8t_morphyRR#R|RRr(RR0RGtanalysestfirst((R0R$s0/p/zhu/06/nlp/nltk/nltk/corpus/reader/wordnet.pyR$s  # RtsestvestftxesRtzestztchestchtshestshtmentmantiestytesRtedtingtertestc#si}ifd||jox||D] }|VqJWntjo$|idod}|d }nd}x|D]}||VqWdS(Nc3s|ijoi|jo |VnxSD]K\}}|i|o/x,|t| |D] }|VqlWq4q4WdS(N(RtendswithRr(R0toldtnewR6(RRGt substitutionsttry_substitutions(s0/p/zhu/06/nlp/nltk/nltk/corpus/reader/wordnet.pyRKs  tfuliR(RtMORPHOLOGICAL_SUBSTITUTIONSRRG(RR0RGt exceptionsR6R((RRGRJRKs0/p/zhu/06/nlp/nltk/nltk/corpus/reader/wordnet.pyR1s      g?cCst}x!|iD]}|i|qWh}xtD]}tt||sman(R@RA(RR(R@RA(sesR(sesR(sedR(sedR(RDR(RDR(RER(RFR(RER(RFR(,R R RR8RRRtADVRtVERBRR RRRRR=RRRRRR3RRARRRCR'R(RRJRRRQRRRRRR$RMR1R(((s0/p/zhu/06/nlp/nltk/nltk/corpus/reader/wordnet.pyRsb*3 ) +    ! y    >       #     tWordNetICCorpusReadercBs eZdZdZdZRS(sE A corpus reader for the WordNet information content corpus. cCsti|||dS(N(RR=(RRtfileids((s0/p/zhu/06/nlp/nltk/nltk/corpus/reader/wordnet.pyR=vsc Csh}tt|t}x5|D]-} | |i|jo|i| qUqUWqHW|o dG|GHng} |D]} | |jo | | qq~ }|o dG|GHnx~|D]v} | i} d}x>| D]6}|djpt||jot|}q q W||jo|}| }qqW|o dG|GHn|S(s Finds the least common subsumer of two synsets in a WordNet taxonomy, where the least common subsumer is defined as the ancestor node common to both input synsets whose shortest path to the root node is the longest. @type synset1: L{Synset} @param synset1: First input synset. @type synset2: L{Synset} @param synset2: Second input synset. @return: The ancestor synset common to both input synsets which is also the LCS. is > Subsumers1:cSs |iS((R(R((s0/p/zhu/06/nlp/nltk/nltk/corpus/reader/wordnet.pytss > Eliminated:s > Subsumers2:is> LCS Subsumer by depth:(R8R~RWRqRZRsRr(RRRRtmax_min_path_lengthRt eliminatedthypernym_relationts1ts2RFRt candidatet paths_to_roottmin_path_lengthtpath((s0/p/zhu/06/nlp/nltk/nltk/corpus/reader/wordnet.pyt _lcs_by_depths@     2     cs|i|ijotdd||fnt|}t|}|i|}t|djo d}ntfd|D}|o dG|GHn|||fS(s Get the information content of the least common subsumer that has the highest information content value. If two nodes have no explicit common subsumer, assume that they share an artificial root node that is the hypernym of all explicit roots. @type synset1: L{Synset} @param synset1: First input synset. @type synset2: L{Synset} @param synset2: Second input synset. Must be the same part of speech as the first synset. @type ic: C{dict} @param ic: an information content object (as returned by L{load_ic()}). @return: The information content of the two synsets and their most informative subsumer s-Computing the least common subsumer requires s*%s and %s to have the same part of speech.ic3s"x|]}t|VqWdS(N(tinformation_content(ReR(R(s0/p/zhu/06/nlp/nltk/nltk/corpus/reader/wordnet.pys ss> LCS Subsumer by content:(RGR RpR~RrRh(RRRRRRRt subsumer_ic((Rs0/p/zhu/06/nlp/nltk/nltk/corpus/reader/wordnet.pyRs  cCs|y||i}Wn,tj o d}t||inX||i}|djotSnti||d SdS(Ns>Information content file has no entries for part-of-speech: %si(RGRR RHRRR(R3RticpostmsgRT((s0/p/zhu/06/nlp/nltk/nltk/corpus/reader/wordnet.pyRp#s  cCsH|ddjotSn,|ddjotSnd}t|dS(NiR R s?Unidentified part of speech in WordNet Information Content file(RR]R(tfieldRs((s0/p/zhu/06/nlp/nltk/nltk/corpus/reader/wordnet.pyRa4s c sddk}dGHt|iid}dGH|i|i}dGHd}|iG|iG|iGH|i GH|i GH|i GHdg}dd d d g}fd }||}||}t ||} |GH|GHd GHdi GHdi GHdi GH|diGH|diGH|diGH|diGHdiGHdiGHdi GHdiGHdiGHdiGHdiGHdiGHdiGHdiGHdiGHdiGH|d iGH|d!iGHd"iGHd#iGHd$iGHd#iGH|d%iGHd&iGHd'iGHd(iGHd&idGHdid)GHdi d)GHdi!d)GHt"|iid*d+} | i#d,} di$d)| GH| i#d-} di%d)| GHdS(.Nisloading wordnetscorpora/wordnets done loadingsgetting a synset for gosgo.v.21szap.n.01szap.v.01szap.v.02s nuke.v.01smicrowave.v.01cs%g}|D]}||q ~S(N((tsynset_stringsRFR3(tS(s0/p/zhu/06/nlp/nltk/nltk/corpus/reader/wordnet.pyt _get_synsetsTss Navigations:s travel.v.01s travel.v.02s travel.v.03s zap.v.03.nukeszap.v.03.atomizeszap.v.03.atomises zap.v.03.zapsdog.n.01s breakfast.n.1smeal.n.1s Austen.n.1s composer.n.1s faculty.n.2s copilot.n.1s table.n.2s course.n.7s water.n.1sgin.n.1sleader.n.1.leadersincrease.v.1.increases snore.v.1s heavy.a.1s light.a.1sEnglish.a.1.Englishs person.n.01s sail.v.01s fall.v.12scat.n.01scorpora/wordnet_ics.*\.dats ic-brown.dats ic-semcor.dat(&tnltkRtdatatfindR3RR+RGRVRRRTRURWRRORRRRRRRRRRMR!R)RRPRbRRRRR^RRR( RxtwntLt move_synsettzap_ntzap_vRwt zap_n_synsetst zap_v_synsetst zap_synsetstwnicR((Rvs0/p/zhu/06/nlp/nltk/nltk/corpus/reader/wordnet.pytdemoBsx        t__main__(-RRt itertoolsRRt nltk.compatRtnltk.corpus.readerRRmRRtnltk.probabilityRRRRR\RR]R#R8R t ExceptionR tobjectRR0RQRR^RRRRRRRRRoRRpRaRR (((s0/p/zhu/06/nlp/nltk/nltk/corpus/reader/wordnet.pys s  $ =Zq3       ; &   S