VhHedZddlZddlZddlZddlZddlmZmZddlm Z ddl m Z m Z ddl mZddlmZddlmZdd lmZdd lmZd Zd \ZZZZZeeeegZd Zej@dZ!Gdde"Z#e GddZ$Gdde$Z%Gdde$Z&GddeZ'GddeZ(d$dZ)d$dZ*d$dZ+d%dZ,d%dZ-d%d Z.e&jRje)_e&jTje*_e&jVje+_e&jXje,_e&jZje-_e&j\je._d%d!Z/d"Z0d#Z1y)&a An NLTK interface for WordNet WordNet is a lexical database of English. Using synsets, helps find conceptual relationships between words such as hypernyms, hyponyms, synonyms, antonyms etc. For details about WordNet see: https://wordnet.princeton.edu/ This module also allows you to find lemmas in languages other than English from the Open Multilingual Wordnet https://omwn.org/ N) defaultdictdeque)total_ordering)chainislice) itemgetter) CorpusReader) deprecated)FreqDist)binary_search_filegu<7~asrnv)(Nz Something %sz Somebody %sz It is %singzSomething is %sing PPz%Something %s something Adjective/NounzSomething %s Adjective/NounzSomebody %s AdjectivezSomebody %s somethingzSomebody %s somebodyzSomething %s somebodyzSomething %s somethingzSomething %s to somebodyzSomebody %s on somethingzSomebody %s somebody somethingz!Somebody %s something to somebodyz#Somebody %s something from somebodyz#Somebody %s somebody with somethingz!Somebody %s somebody of somethingz!Somebody %s something on somebodyzSomebody %s somebody PPzSomebody %s something PPzSomebody %s PPzSomebody's (body part) %sz"Somebody %s somebody to INFINITIVEzSomebody %s somebody INFINITIVEzSomebody %s that CLAUSEzSomebody %s to somebodyzSomebody %s to INFINITIVEzSomebody %s whether INFINITIVEz)Somebody %s somebody into V-ing somethingz$Somebody %s something with somethingzSomebody %s INFINITIVEzSomebody %s VERB-ingzIt %s that CLAUSEzSomething %s INFINITIVEzSomebody %s at somethingzSomebody %s for somethingzSomebody %s on somebodyzSomebody %s out of somebodyz \.[\d]+\.ceZdZdZy) WordNetErrorz.An exception class for wordnet-related errors.N)__name__ __module__ __qualname____doc__J/home/dcms/DCMS/lib/python3.12/site-packages/nltk/corpus/reader/wordnet.pyrr~s8rrceZdZdZdZdZdZdZdZdZ dZ d Z d Z d Z d Zd ZdZdZdZdZdZdZdZdZdZdZdZdZdZdZdZdZy)_WordNetObjectz+A common base class for lemmas and synsets.c$|jdSN@_relatedselfs r hypernymsz_WordNetObject.hypernyms}}S!!rc$|jdSrr!r#s r _hypernymsz_WordNetObject._hypernymsr&rc$|jdSNz@ir!r#s rinstance_hypernymsz!_WordNetObject.instance_hypernyms}}T""rc$|jdSr*r!r#s r_instance_hypernymsz"_WordNetObject._instance_hypernymsr,rc$|jdS)N~r!r#s rhyponymsz_WordNetObject.hyponymsr&rc$|jdS)Nz~ir!r#s rinstance_hyponymsz _WordNetObject.instance_hyponymsr,rc$|jdS)Nz#mr!r#s rmember_holonymsz_WordNetObject.member_holonymsr,rc$|jdS)Nz#sr!r#s rsubstance_holonymsz!_WordNetObject.substance_holonymsr,rc$|jdS)Nz#pr!r#s r part_holonymsz_WordNetObject.part_holonymsr,rc$|jdS)Nz%mr!r#s rmember_meronymsz_WordNetObject.member_meronymsr,rc$|jdS)Nz%sr!r#s rsubstance_meronymsz!_WordNetObject.substance_meronymsr,rc$|jdS)Nz%pr!r#s r part_meronymsz_WordNetObject.part_meronymsr,rc$|jdS)Nz;cr!r#s r topic_domainsz_WordNetObject.topic_domainsr,rc$|jdS)Nz-cr!r#s rin_topic_domainsz_WordNetObject.in_topic_domainsr,rc$|jdS)Nz;rr!r#s rregion_domainsz_WordNetObject.region_domainsr,rc$|jdS)Nz-rr!r#s rin_region_domainsz _WordNetObject.in_region_domainsr,rc$|jdS)Nz;ur!r#s r usage_domainsz_WordNetObject.usage_domainsr,rc$|jdS)Nz-ur!r#s rin_usage_domainsz_WordNetObject.in_usage_domainsr,rc$|jdS)N=r!r#s r attributesz_WordNetObject.attributesr&rc$|jdS)N*r!r#s r entailmentsz_WordNetObject.entailmentsr&rc$|jdS)N>r!r#s rcausesz_WordNetObject.causesr&rc$|jdS)N^r!r#s r also_seesz_WordNetObject.also_seesr&rc$|jdS)N$r!r#s r verb_groupsz_WordNetObject.verb_groupsr&rc$|jdS)N&r!r#s r similar_tosz_WordNetObject.similar_tosr&rc,t|jSN)hash_namer#s r__hash__z_WordNetObject.__hash__sDJJrc4|j|jk(Sr_rar$others r__eq__z_WordNetObject.__eq__zzU[[((rc4|j|jk7Sr_rdres r__ne__z_WordNetObject.__ne__rhrc4|j|jkSr_rdres r__lt__z_WordNetObject.__lt__szzEKK''rN) rrrrr%r(r+r.r1r3r5r7r9r;r=r?rArCrErGrIrKrNrQrTrWrZr]rbrgrjrlrrrrrs5""##"#############"""""" ))(rrcleZdZdZgdZdZdZdZdZdZ dZ d Z d Z d Z d Zd ZdZdZdZy)Lemmaa The lexical entry for a single morphological form of a sense-disambiguated word. Create a Lemma from a "..." string where: is the morphological stem identifying the synset is one of the module attributes ADJ, ADJ_SAT, ADV, NOUN or VERB is the sense number, counting from 0. is the morphological form of interest Note that and can be different, e.g. the Synset 'salt.n.03' has the Lemmas 'salt.n.03.salt', 'salt.n.03.saltiness' and 'salt.n.03.salinity'. Lemma attributes, accessible via methods with the same name: - name: The canonical name of this lemma. - synset: The synset that this lemma belongs to. - syntactic_marker: For adjectives, the WordNet string identifying the syntactic position relative modified noun. See: https://wordnet.princeton.edu/documentation/wninput5wn For all other parts of speech, this attribute is None. - count: The frequency of this lemma in wordnet. Lemma methods: Lemmas have the following methods for retrieving related Lemmas. They correspond to the names for the pointer symbols defined here: https://wordnet.princeton.edu/documentation/wninput5wn These methods all return lists of Lemmas: - antonyms - hypernyms, instance_hypernyms - hyponyms, instance_hyponyms - member_holonyms, substance_holonyms, part_holonyms - member_meronyms, substance_meronyms, part_meronyms - topic_domains, region_domains, usage_domains - attributes - derivationally_related_forms - entailments - causes - also_sees - verb_groups - similar_tos - pertainyms _wordnet_corpus_readerra_syntactic_marker_synset_frame_strings _frame_ids_lexname_index_lex_id_lang_keyc||_||_||_||_g|_g|_||_||_d|_d|_ yNengro)r$wordnet_corpus_readersynsetname lexname_indexlex_idsyntactic_markers r__init__zLemma.__init__sN'<# !1  +   rc|jSr_rdr#s rr~z Lemma.name- zzrc|jSr_)rqr#s rrzLemma.syntactic_marker0s%%%rc|jSr_)rrr#s rr}z Lemma.synset3 ||rc|jSr_)rsr#s r frame_stringszLemma.frame_strings6s"""rc|jSr_rtr#s r frame_idszLemma.frame_ids9 rc|jSr_)rwr#s rlangz Lemma.lang<rrc|jSr_)rxr#s rkeyz Lemma.key? yyrcxt|j|jj|jf}d|zS)Nz %s('%s.%s'))typerrrra)r$tups r__repr__zLemma.__repr__Bs14j!!4<<#5#5tzzAs""rc &|jj}|j|f|jjvrgS|jj|j|fDcgc]\}}}|||j |c}}}Scc}}}wr_)rpsynset_from_pos_and_offsetrarr_lemma_pointers_lemmas)r$relation_symbol get_synsetposoffset lemma_indexs rr"zLemma._relatedFs00KK JJ ( 0L0L LI-1LL,H,H O+-  (V[ sF # + +K 8   s&!B c8|jj|S))Return the frequency count for this Lemma)rp lemma_countr#s rcountz Lemma.countQs**66t<.." 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, accessible via methods with the same name: - 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: https://wordnet.princeton.edu/documentation/wninput5wn 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 ) _pos_offsetrartr _lemma_names _definition _examples_lexname _pointersr _max_depth _min_depthc||_d|_d|_d|_g|_g|_g|_d|_g|_d|_ d|_ tt|_ tt|_yr_)rprrrartrrrrr_all_hypernymsrsetrlistr)r$r|s rrzSynset.__init__sn&;#     "$S)*40rc|jSr_)rr#s rrz Synset.posrrc|jSr_)rr#s rrz Synset.offsetrrc|jSr_rdr#s rr~z Synset.namerrc|jSr_rr#s rrzSynset.frame_idsrrc|j}||jvry|dk(r|S|j||j|}|jj |}||j ||vr|j |||Sy)z7Helper method for Synset.definition and Synset.examplesNr{)rplangs_load_lang_datass2oflg_attrsindex _lang_data)r$doc_typedefaultrcorpusofis r_docz Synset._docs,, v||~ % U]N  " "4 (d#B%%h/AV&&t,Q//((.q1"55rc>|jd|j|S)z'Return definition in specified languagedefr)rrr$rs r definitionzSynset.definitionsyy 0 0ty<|jd|j|S)z%Return examples in specified languageexer)rrrs rexampleszSynset.examplessyyTy::rc|jSr_)rr#s rlexnamezSynset.lexnames }}rcf|jtk(r|jjdk7ryy)Nz1.6FT)rNOUNrp get_versionr#s r _needs_rootzSynset._needs_roots* 99 !z#Synset.max_depth..,)KA!++-)K)__dict__r%r+rmaxr$r%s rrzSynset.max_depth!c t}} ,(4+B+B+DDI"##$c)K)K&K"Krcd|jvrP|j|jz}|sd|_|jSdt d|Dz|_|jS)zm :return: The length of the shortest hypernym path from this synset to the root. rrrc3<K|]}|jywr_ min_depthrs rrz#Synset.min_depth..:rr)rr%r+rminrs rrzSynset.min_depth/rrc#JKddlm}||||D] }||k7s |yw)a Return the transitive closure of source under the rel relationship, breadth-first, discarding cycles: >>> from nltk.corpus import wordnet as wn >>> computer = wn.synset('computer.n.01') >>> topic = lambda s:s.topic_domains() >>> print(list(computer.closure(topic))) [Synset('computer_science.n.01')] UserWarning: Discarded redundant search for Synset('computer.n.01') at depth 2 Include redundant paths (but only once), avoiding duplicate searches (from 'animal.n.01' to 'entity.n.01'): >>> dog = wn.synset('dog.n.01') >>> hyp = lambda s:sorted(s.hypernyms()) >>> print(list(dog.closure(hyp))) [Synset('canine.n.02'), Synset('domestic_animal.n.01'), Synset('carnivore.n.01'), Synset('animal.n.01'), Synset('placental.n.01'), Synset('organism.n.01'), Synset('mammal.n.01'), Synset('living_thing.n.01'), Synset('vertebrate.n.01'), Synset('whole.n.02'), Synset('chordate.n.01'), Synset('object.n.01'), Synset('physical_entity.n.01'), Synset('entity.n.01')] UserWarning: Discarded redundant search for Synset('animal.n.01') at depth 7 r)acyclic_breadth_firstN) nltk.utilr)r$reldepthrr}s rclosurezSynset.closure=s/: 4+D#u= F~  s##r)acyclic_depth_first) unweighted_minimum_spanning_treeNc$ddlm}|||||S)a# Return the full relation tree, including self, discarding cycles: >>> from nltk.corpus import wordnet as wn >>> from pprint import pprint >>> computer = wn.synset('computer.n.01') >>> topic = lambda s:sorted(s.topic_domains()) >>> pprint(computer.tree(topic)) [Synset('computer.n.01'), [Synset('computer_science.n.01')]] UserWarning: Discarded redundant search for Synset('computer.n.01') at depth -3 But keep duplicate branches (from 'animal.n.01' to 'entity.n.01'): >>> dog = wn.synset('dog.n.01') >>> hyp = lambda s:sorted(s.hypernyms()) >>> pprint(dog.tree(hyp)) [Synset('dog.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')]]]]]]]]]]]]], [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')]]]]]]]]] r)acyclic_branches_depth_first)rr)r$rr cut_markrs rtreez Synset.treefsV ;+D#uhGGrcg}|j|jz}t|dk(r|gg}|D]9}|jD]$}|j ||j |&;|S)a% 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 ``Synset`` node and a root node. r)r%r+lenhypernym_pathsr)r$pathsr%hypernym ancestor_lists rrzSynset.hypernym_pathssNN$t'>'>'@@ y>Q VHE! ,H!)!8!8!: , $$T* ]+ , , rcV|js,|jDchc] }|D]}| c}}|_|js,|jDchc] }|D]}| c}}|_t|jj|jScc}}wcc}}w)z Find all synsets that are hypernyms of this synset and the other synset. :type other: Synset :param other: other input synset. :return: The synsets that are hypernyms of both synsets. )r_iter_hypernym_listsr intersection)r$rf self_synsets self_synset other_synsets other_synsets rcommon_hypernymszSynset.common_hypernymss""%)$=$=$?# #/# ##D  ##&+%?%?%A$!$1$!$$E D''44U5I5IJKK# $s BB%c|j|}|r3td}d|_d|_d|_|j | |r6t d|D}|Dcgc]}|j|k(s|}}n5t d|D}|Dcgc]}|j|k(s|}}t|Scc}wcc}w#t$rgcYSwxYw)aN Get a list of lowest synset(s) that both synsets have as a hypernym. When `use_min_depth == False` this means that the synset which appears as a hypernym of both `self` and `other` with the lowest maximum depth is returned or if there are multiple such synsets at the same depth they are all returned However, if `use_min_depth == True` then the synset(s) which has/have the lowest minimum depth and appear(s) in both paths is/are returned. By setting the use_min_depth flag to True, the behavior of NLTK2 can be preserved. This was changed in NLTK3 to give more accurate results in a small set of cases, generally with synsets concerning people. (eg: 'chef.n.01', 'fireman.n.01', etc.) This method is an implementation of Ted Pedersen's "Lowest Common Subsumer" method from the Perl Wordnet module. It can return either "self" or "other" if they are a hypernym of the other. :type other: Synset :param other: other input synset :type simulate_root: bool :param simulate_root: The various verb taxonomies do not share a single root which disallows this metric from working for synsets that are not connected. This flag (False by default) creates a fake root that connects all the taxonomies. Set it to True to enable this behavior. For the noun taxonomy, there is usually a default root except for WordNet version 1.6. If you are using wordnet 1.6, a fake root will need to be added for nouns as well. :type use_min_depth: bool :param use_min_depth: This setting mimics older (v2) behavior of NLTK wordnet If True, will use the min_depth function to calculate the lowest common hypernyms. This is known to give strange results for some synset pairs (eg: 'chef.n.01', 'fireman.n.01') but is retained for backwards compatibility :return: The synsets that are the lowest common hypernyms of both synsets N*ROOT*cgSr_rrrrz0Synset.lowest_common_hypernyms..sBrcgSr_rrrrr"z0Synset.lowest_common_hypernyms..sRrc3<K|]}|jywr_rrrs rrz1Synset.lowest_common_hypernyms..?! ?rc3<K|]}|jywr_rr%s rrz1Synset.lowest_common_hypernyms..r&r) rrrar%r+rrrrsorted ValueError) r$rf simulate_root use_min_depthsynsets fake_synsetrr unsorted_lchs rlowest_common_hypernymszSynset.lowest_common_hypernymssP''.  ,K (K $.K !-7K * NN; ' ?w?? +2Qaakkmy6PQ Q?w?? +2Qaakkmy6PQ Q,' ' R R I s<C C9C=C C/C3 C C CCc||fh}|j|jzD]}||j|dzdz}|rAtd}d|_t |t dd}|j||dzf|S)a Get the path(s) from this synset to the root, counting the distance of each node from the initial node on the way. A set of (synset, distance) tuples is returned. :type distance: int :param distance: the distance (number of edges) from this hypernym to the original hypernym ``Synset`` on which this method was called. :return: A set of ``(Synset, int)`` tuples where each ``Synset`` is a hypernym of the first ``Synset``. rFr*Nr )r)r(r.hypernym_distancesrrarrr)r$distancer* distancesrr-fake_synset_distances rr2zSynset.hypernym_distancessH%& )D,D,D,FF XH 44X\QV4W WI X  ,K (K #&yjm#DQ#G MM;(.s@##u@c3&K|]}|f ywr_rr8s rrz2Synset._shortest_hypernym_paths.. sI##uIr:) rarpopleftrr(r.rrvalues)r$r*queuepathrr-r s @r_shortest_hypernym_pathszSynset._shortest_hypernym_pathss :: !!9 ayk"}}HAuDyDG QJE LL@@ @ LLI1F1F1HI I  ,K (K  #DKKM 2Q 6D  rc||k(ry|j|}|j|}td}|}|jD]&\}}|j||} t ||| z}(t j |rdS|S)a Returns the distance of the shortest path linking the two synsets (if one exists). For each synset, all the ancestor nodes and their distances are recorded and compared. The ancestor node common to both synsets that can be reached with the minimum number of traversals is used. If no ancestor nodes are common, None is returned. If a node is compared with itself 0 is returned. :type other: Synset :param other: The Synset to which the shortest path will be found. :return: The number of edges in the shortest path connecting the two nodes, or None if no path exists. rinfN)r@floatitemsgetrmathisinf) r$rfr* dist_dict1 dist_dict2rB path_distancer}d1d2s rshortest_path_distancezSynset.shortest_path_distance)s 5=22=A 33MB El $**, 8JFB,B rBw7M 8zz-0tCmCrc|j||xr"|jxs|j}||dkryd|dzz S)a Path Distance Similarity: Return a score denoting how similar two word senses are, based on the shortest path that connects the senses in the is-a (hypernym/hypnoym) taxonomy. The score is in the range 0 to 1, except in those cases where a path cannot be found (will only be true for verbs as there are many distinct verb taxonomies), in which case None is returned. A score of 1 represents identity i.e. comparing a sense with itself will return 1. :type other: Synset :param other: The ``Synset`` that this ``Synset`` is being compared to. :type simulate_root: bool :param simulate_root: The various verb taxonomies do not share a single root which disallows this metric from working for synsets that are not connected. This flag (True by default) creates a fake root that connects all the taxonomies. Set it to false to disable this behavior. For the noun taxonomy, there is usually a default root except for WordNet version 1.6. If you are using wordnet 1.6, a fake root will be added for nouns as well. :return: A score denoting the similarity of the two ``Synset`` objects, normally between 0 and 1. None is returned if no connecting path could be found. 1 is returned if a ``Synset`` is compared with itself. r1Nr?r)rMr)r$rfverboser*r3s rpath_similarityzSynset.path_similarityJs[6.. 'WT-=-=-?-V5CTCTCV/   x!|hl##rc|j|jk7rtd|d|d|j}|j|jjvr&|jj |j||jj|j}|j ||xr|}| |dks|dk(rytj|dzd|zz  S) ab 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: Synset :param other: The ``Synset`` that this ``Synset`` is being compared to. :type simulate_root: bool :param simulate_root: The various verb taxonomies do not share a single root which disallows this metric from working for synsets that are not connected. This flag (True by default) creates a fake root that connects all the taxonomies. Set it to false to disable this behavior. For the noun taxonomy, there is usually a default root except for WordNet version 1.6. If you are using wordnet 1.6, a fake root will be added for nouns as well. :return: A score denoting the similarity of the two ``Synset`` objects, normally greater than 0. None is returned if no connecting path could be found. If a ``Synset`` is compared with itself, the maximum score is returned, which varies depending on the taxonomy depth. z&Computing the lch similarity requires  and ! to have the same part of speech.r1Nrr@) rrrrpr_compute_max_depthrMrFlog)r$rfrPr* need_rootr r3s rlch_similarityzSynset.lch_similarityms6 99 "@DeM  $$& 99D77BB B  ' ' : :499i P++66tyyA.. !<9/   x!|uz(Q,3;7888rc`|jxs|j}|j||xr|d}t|dk(ry||vr|n|d}|jdz}|j ||xr|}|j ||xr|} || y||z }| |z } d|z|| zz S)a Wu-Palmer Similarity: Return a score denoting how similar two word senses are, based on the depth of the two senses in the taxonomy and that of their Least Common Subsumer (most specific ancestor node). Previously, the scores computed by this implementation did _not_ always agree with those given by Pedersen's Perl implementation of WordNet Similarity. However, with the addition of the simulate_root flag (see below), the score for verbs now almost always agree but not always for nouns. The LCS does not necessarily feature in the shortest path connecting the two senses, as it is by definition the common ancestor deepest in the taxonomy, not closest to the two senses. Typically, however, it will so feature. Where multiple candidates for the LCS exist, that whose shortest path to the root node is the longest will be selected. Where the LCS has multiple paths to the root, the longer path is used for the purposes of the calculation. :type other: Synset :param other: The ``Synset`` that this ``Synset`` is being compared to. :type simulate_root: bool :param simulate_root: The various verb taxonomies do not share a single root which disallows this metric from working for synsets that are not connected. This flag (True by default) creates a fake root that connects all the taxonomies. Set it to false to disable this behavior. For the noun taxonomy, there is usually a default root except for WordNet version 1.6. If you are using wordnet 1.6, a fake root will be added for nouns as well. :return: A float score denoting the similarity of the two ``Synset`` objects, normally greater than zero. If no connecting path between the two senses can be found, None is returned. T)r*r+rNrr1rU)rr/rrrM) r$rfrPr*rX subsumerssubsumerr len1len2s rwup_similarityzSynset.wup_similaritysF$$&=%*;*;*= 00 !<9D1 y>Q 9,4)A, ""$q(** M$?i+ ++ M$?i,  <4<    e t ,,rc(t|||\}}}|S)a Resnik Similarity: Return a score denoting how similar two word senses are, based on the Information Content (IC) of the Least Common Subsumer (most specific ancestor node). :type other: Synset :param other: The ``Synset`` that this ``Synset`` is being compared to. :type ic: dict :param ic: an information content object (as returned by ``nltk.corpus.wordnet_ic.ic()``). :return: A float score denoting the similarity of the two ``Synset`` objects. Synsets whose LCS is the root node of the taxonomy will have a score of 0 (e.g. N['dog'][0] and N['table'][0]). _lcs_icr$rficrPic1ic2lcs_ics rres_similarityzSynset.res_similaritys"#43S& rc||k(rtSt|||\}}}|dk(s|dk(ry||zd|zz }|dk(rtSd|z S)a 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: Synset :param other: The ``Synset`` that this ``Synset`` is being compared to. :type ic: dict :param ic: an information content object (as returned by ``nltk.corpus.wordnet_ic.ic()``). :return: A float score denoting the similarity of the two ``Synset`` objects. rr)_INFrb)r$rfrdrPrerfrg ic_differences rjcn_similarityzSynset.jcn_similaritysa" 5=K"43S& !8saxc AJ. A K=  rc:t|||\}}}d|z||zz S)a Lin Similarity: Return a score denoting how similar two word senses are, based on the Information Content (IC) of the Least Common Subsumer (most specific ancestor node) and that of the two input Synsets. The relationship is given by the equation 2 * IC(lcs) / (IC(s1) + IC(s2)). :type other: Synset :param other: The ``Synset`` that this ``Synset`` is being compared to. :type ic: dict :param ic: an information content object (as returned by ``nltk.corpus.wordnet_ic.ic()``). :return: A float score denoting the similarity of the two ``Synset`` objects, in the range 0 to 1. rUrarcs rlin_similarityzSynset.lin_similarity s,"#43S&f s++rc#K|g}t}|r[|D]}|j|||Dcgc].}|j|jzD]}||vr| 0}}}|rZyycc}}ww)z :return: An iterator over ``Synset`` objects that are either proper hypernyms or instance of hypernyms of the synset. N)rrr%r+)r$rrr}rs rrzSynset._iter_hypernym_lists4s vu !  !J#!'!1!1!3f6O6O6Q!Q4'D s1A43A.&A4,A4cLt|jd|jdS)Nz('z'))rrrar#s rrzSynset.__repr__Fs$t*%%&b B77rc|jj}||jvrgS|j|}|Dcgc]\}}|||}}}|Scc}}wr_)rprr)r$rrpointer_tuplesrrrs rr"zSynset._relatedIs[00KK $.. 0I88F GfZV $ G G HsAr{))ruN)FF)rFFFT)*rrrrrrrrr~rrrrrrrrrrrr rr  acyclic_treer mstrrrr/r2r@rMrQrYr_rhrmrorrr"rrrrr_s2hI 1& =; *6  !F>A -H^(L.9v,0DB!$F.9`K-Z( !D,($8rrc eZdZdZdZd\ZZZZZ ededede diZ ede d ed ed ed iZ e d e jDZdZfdZdJdZdKdZdKdZdKdZdKdZdKdZdZdZdZdZdZdZdZdZdZd Z d!Z!dLd"Z"d#Z#d$Z$d%Z%d&Z&e'd'd(Z(d)Z)d*Z*dMd+Z+dNd,Z,dNd-Z-dOd.Z.dNd/Z/dJd0Z0dLd1Z1dLd2Z2dPd3Z3dLd4Z4dLd5Z5dLd6Z6d7Z7dQd9Z8e9jpje8_dQd:Z:e9jtje:_dQd;Z;e9jvje;_dRd<ZZ>e9j|je>_dSd?Z?egd@e gdAegdBegiZ@e@ee@e<dTdCZAdUdDZBdEZCdFZDdGddHddd8fdIZExZFS)VWordNetCorpusReaderzA A corpus reader used to access wordnet or its variants. utf8r adjadvnounverbrrjc#,K|] }|dddyw)Nrur)rrs rrzWordNetCorpusReader.hs@Cc$B$i@s) cntlist.revlexnames index.sensez index.adjz index.advz index.nounz index.verbzdata.adjzdata.advz data.nounz data.verbzadj.exczadv.excznoun.exczverb.excct |||j|jt t |_t t |_t t |_||_ d|_ t t|_ d|jd<|jtjdt|_t t"|_i|_i|_g|_d|_d|_|j1d5}t3|D]D\}}|j5\}}}t7||k(sJ|j*j9|F ddd|j;|j=i|_i|_ i|_!|jE|_#gd|_$y#1swY]xYw)z_ Construct a new wordnet corpus reader, with the given root directory. encodingNr{zFThe multilingual functions are not available with this Wordnet versionr)lemmarrr)%superr_FILES _ENCODINGrdict_lemma_pos_offset_map_synset_offset_cacher _omw_reader _exomw_readerstr provenanceswarningswarnr omw_langsrr_data_file_map_exception_mapr_key_count_file_key_synset_fileopen enumeratesplitintr_load_lemma_pos_offset_map_load_exception_mapnomapsplitsmergesmap_wnmap30r) r$root omw_readerfprlinerr_ __class__s rrzWordNetCorpusReader.__init__s t{{T^^D&1%6"%0$5!&d+&"&s+"$    # MMX &d+  # $YYz " /b$R= /4$(JJL!w5zQ&%%g. / / '')   "   [[] 6 ) / /s AF==GNcxd}|rddlm}m}|||d|z}n|}|j|5}i}|D]n}|j j } | d} |j t| j ddj dd} | dd| || <p d d d |S#1swYSxYw) zMRead sense key to synset id mapping from index.sense file in corpus directoryrr)r LazyCorpusLoaderz.*/%r:-N) nltk.corpusr rrstripr _pos_namesr) r$versionfnr rixreaderr sensekey_maprfieldssensekeyrs r index_sensezWordNetCorpusReader.index_senses   B'v{KHH ]]2  >"L >++-!!9ooc(..*=a*@*F*Fs*KA*N&OP,21I;au)= X&  > > >s A6B//B9cV|j|}|j}i}t|jD]}g||< t|jj t|jD] }||}||}||j |"|Sr_)rrr=keysrr) r$r sensekey_map1 sensekey_map2synset_to_manysynsetidrsourcetargets r map_to_manyzWordNetCorpusReader.map_to_manys((1 ((* M0023 *H')N8 $ *M..01>>  ""$ %  2H#8,F"8,F 6 " ) )& 1  2 rct|j|<i|j|<|j|}i}|D]}||}|rt|}t |dk(r|d}nKg}|D]$} |j |j | | f&||j||<t|d}|||<|ddk(s|||ddd<|j|j||S)Nrrrurr) rrrrrrrrr) r$rr synset_to_onercandidates_bagcandidates_setrcounts candidates r map_to_onezWordNetCorpusReader.map_to_ones!e 7! G))'2 $ 0F+F3N!$^!4~&!++A.FF%3T  ~';';I'F &RST39DKK(0 [^F(. f%":$8>MVCR[M"34 7#''/% 0&rcL|j|k(ry|j|S)zBMapping from Wordnet 'version' to currently loaded Wordnet versionN)rr)r$rs rrzWordNetCorpusReader.map_wns%     (??7+ +rc^||jvr|j|}|j|Sr_)rr)r$r_mymaps r split_synsetsz!WordNetCorpusReader.split_synsetss+ $++ %__W-F{{7##rcj||jvrtt}|j|j D] \}}|D]}||j |"|j Dcic]\}}t |dkDs||c}}|j|<|j|Scc}}w)Nr)rrrrrDrr)r$rmergertargetsrtrgsrcs rmerged_synsetsz"WordNetCorpusReader.merged_synsets s $++ %$E#'#3#3G#<#B#B#D .%.F&M%%f-. .*/$%S##c(Q,S$DKK {{7##$s 3B/ B/cD|j|dt|ddS)z!take an id and return the synsetsruN)rr)r$rs rof2sszWordNetCorpusReader.of2sss#..r"vs2bq6{CCrcP|r$|jdd|jSy)zreturn the ID of the synset08drN)rr)r$sss rrzWordNetCorpusReader.ss2ofs* iik#&az2 2 rc  ||jvry|jr|js|j||j vr t d|j r||jvr |j }n |j}|j|}|dvr|}nd}|j|d|d|jddd 5}|j||ddd|j|y#1swYxYw) z^load the wordnet data of the requested language from the file to the cache, _lang_dataNzLanguage is not supported.cldrwiktdataz/wn-rrr.tab) rrradd_omwrrrrrr custom_lemmasdisable_custom_lemmas)r$rrprovprov2rs rrz#WordNetCorpusReader._load_lang_data s 4?? "    DNN LLN tzz| #;< <   $dnn"<''F%%F% # #EE [[D6eWAdjjoa.@-AF G )2   r4 ( ) ""4( ) )s C99Dc>|j}|D]}tjj|\}}tjj |\}}|dk(sM|jdd}||j vs|dvr|d|}||j |<y)zDAdd languages from Multilingual Wordnet to the provenance dictionaryrrrurrN)fileidsosr?rsplitextr) r$rrfileidrlangfile file_namefile_extensionrs r add_provszWordNetCorpusReader.add_provs<s.." .FWW]]62ND((*(8(8(B %I~' s+B/4+++t7G/G#V1TF+D)-  & .rc|j|jt|jj |_yr_)rrrrrrr#s rrzWordNetCorpusReader.add_omwJs/ t''(T--2245rctddlm}|j||_|j |jy)a4 Add languages from Extended OMW >>> import nltk >>> from nltk.corpus import wordnet as wn >>> wn.add_exomw() >>> print(wn.synset('intrinsically.r.01').lemmas(lang="eng_wikt")) [Lemma('intrinsically.r.01.per_se'), Lemma('intrinsically.r.01.as_such')] r) extended_omwN)rrrrr)r$rs r add_exomwzWordNetCorpusReader.add_exomwNs* - ) t))*rcHt|jjS)z._next_tokenls #E{*rrrzfile %s, line %i: %s)_FILEMAPr=rr startswithiterrrrangeAssertionErrorr)rrADJADJ_SAT)r$suffixrrrrrr n_synsets n_pointersrn_sensessynset_offsetserrs @rrz.WordNetCorpusReader._load_lemma_pos_offset_mapbsmm**,. TF:./, T2(}+TGAts+  .E+P + )m%( $6 (1},}&)%7 05j0AB1B$'{}#5(H444$ GLIFV)W#km*<)W)W>LD..u5c:czES2259'BW+T, T, T. T0C*X+J7P)F2a!ea?*+AC+GHaOPI, T, TsUAF 8AE:E .E6E EF +F  EF *F F F  F c`|jjD]a\}}i|j|<|jd|z5}|D]*}|j }|dd|j||d<, dddc|jt |jt <y#1swYxYw)Nz%s.excrr)rrDrrrrr)r$rrrrtermss rrz'WordNetCorpusReader._load_exception_maps==..0 CKC')D   $8f,- CCD JJLE9>qrD'',U1X6C C C C (,':':3'?G$  C Cs 0B$$B- cd}|j|D]} t||j}|r|dz }||j |<y#t$rt |YMwxYw)zy Compute the max depth for the given part of speech. This is used by the lch similarity metric. rrN) all_synsetsrr RuntimeErrorprintr)r$rr*r iis rrVz&WordNetCorpusReader._compute_max_depthsn ""3' B E2<<>2   QJE$   b  sA  A"!A"c|jt}|jd|D]@}tjd|}||j d}|jd|cSy)Nrz#Word[nN]et (\d+|\d+\.\d+) Copyrightr) _data_filerseekresearchgroup)r$fhrmatchrs rrzWordNetCorpusReader.get_versions^ __S !   DIIDdKE ++a.   rctj|j}|d|dz ||d}}|j|}|j |D]}|j |k(s|cSt d|d|)z)Return lemma object that matches the nameNr No lemma z in ) SENSENUM_RErendr}rrar)r$r~r separator synset_name lemma_namer}rs rrzWordNetCorpusReader.lemmas &&t,002 "&Q"7ij9IZ [)]]4( E{{j(  YznDHIIrc|j}|jd\}}|jd\}}}}}|jt|}|j|j d|_t |j|} | std|zt| jd} |j|| } | jD]} | j|k(s| cStd|z)NrrrzNo synset found for key %rrzNo lemma found for for key %r) lowerrrrrr_binary_search_filerrrrx) r$rr! lex_sense pos_numberrrrr synset_linerr}rs rlemma_from_keyz"WordNetCorpusReader.lemma_from_keysiik # # I2;//#2F/ M61aooc*o.  ($(IIm$jX]'!IK  s#B&& D /C D  :DD c|tk(rt}|jj|0d|j|z}|j ||j|<|j|S)ze Return an open file pointer for the data file for the given part of speech. data.%s)rrrrErr)r$rrs rrzWordNetCorpusReader._data_filesd '>C    " "3 ' /s!33F'+yy'8D   $""3''rc||j|vr|j||S|j|}|j||j}|dd}|j r`|ddt t |z zt |k(r6|j||}|j|k(sJ||j||<nd}tjd|d|d|jd|S)a - pos: The synset's part of speech, matching one of the module level attributes ADJ, ADJ_SAT, ADV, NOUN or VERB ('a', 's', 'r', 'n', or 'v'). - offset: The byte offset of this synset in the WordNet dict file for this pos. >>> from nltk.corpus import wordnet as wn >>> print(wn.synset_from_pos_and_offset('n', 1740)) Synset('entity.n.01') Nr0z No WordNet synset found for pos=z at offset=r*r) rrrreadlineisalnumrr_synset_from_pos_and_linerrr)r$rr data_filedata_file_line line_offsetr}s rrz.WordNetCorpusReader.synset_from_pos_and_offsets T..s3 3,,S1&9 9OOC( v"++-$Ra(    !#qS[)9'9":!;CK=II33CHF>>V+ ++5;D % %c *6 2F MMk(r<|jAd j&d }!|!j*}"d|!jBz}#ndx}"}#|j*tDjF|j|jH|jB|"|#f}$d|$zjK|_&|j&d j*jK} |jN| |j}%|%jQ|j}&| |j|&d zf}$d|$z|_|S#t8$rYEwxYw#t:$r} t=d|d| | d} ~ wwxYw)N|z [\"].*?[\"]rz "([^"]*)"z; ctSr_rrsrrzBWordNetCorpusReader._synset_from_pos_and_line.._next_tokenKs E{"rz(.*?)(\(.*\))?$0000rjrrrzline z: z%02dz%s%%%d:%02d:%02d:%s:%sz %s.%s.%02i))rrrrsubfindallrrrrrrrrrrrgroupsrnrrrarrrVERB_FRAME_STRINGSrtrs StopIterationr)rrr]rvr{ _pos_numbersrur#rxrr)(r$rr;r} columns_strglossrrexamplerrn_lemmasrr!rmsyn_markrrsymbolr lemma_ids_str source_index target_indexsource_lemma_namelemma_pointerstups frame_countplus frame_numberframe_string_fmt lemma_numberr  head_lemma head_namehead_idroffsets sense_indexrs( @rr9z-WordNetCorpusReader._synset_from_pos_and_line9sV G!/!5!5!7!=!=c!B KE:@@BJzz,6H# 1  ''0 1",!1!1$!7F **,-E #!/FN .M"nn];FO&-FK;="-H8_ 8(] []B/HH/<'(xxz$ HdFJ vxX%%e,##**5;;7 8[]+J:& =$[]+!m +  F*$$V,00#v?#&}Ra'8"#=#AL#&}QR'8"#=#AL(.|(D(J(J%%+%;%;N)*;V*CDDKKfl ;< =  T!+-0 {+TA&=D3;&;#&{}#5L'9,'G$#&{}b#9L#q())00>%+^^XE!,,33LA!00778H5;;8VWX !'|a/? @((// =,,334Du{{4RS%T4^^ BE{{g%#//1!4<?Q F Gs=I9S6S&DS6& S3/S62S33S66 T?TTc@|j|jS)a  Retrieves synset based on a given sense_key. Sense keys can be obtained from lemma.key() From https://wordnet.princeton.edu/documentation/senseidx5wn: A sense_key is represented as:: lemma % lex_sense (e.g. 'dog%1:18:01::') where lex_sense is encoded as:: ss_type:lex_filenum:lex_id:head_word:head_id :lemma: ASCII text of word/collocation, in lower case :ss_type: synset type for the sense (1 digit int) The synset type is encoded as follows:: 1 NOUN 2 VERB 3 ADJECTIVE 4 ADVERB 5 ADJECTIVE SATELLITE :lex_filenum: name of lexicographer file containing the synset for the sense (2 digit int) :lex_id: when paired with lemma, uniquely identifies a sense in the lexicographer file (2 digit int) :head_word: lemma of the first word in satellite's head synset Only used if sense is in an adjective satellite synset :head_id: uniquely identifies sense in a lexicographer file when paired with head_word Only used if head_word is present (2 digit int) >>> import nltk >>> from nltk.corpus import wordnet as wn >>> print(wn.synset_from_sense_key("drive%1:04:03::")) Synset('drive.n.06') >>> print(wn.synset_from_sense_key("driving%1:04:03::")) Synset('drive.n.06') )r(r})r$ sense_keys rsynset_from_sense_keyz)WordNetCorpusReader.synset_from_sense_keysL""9-4466rc |j}|dk(rm|j}|j}|t}|D cgc]=}|j |||D]%}||j |gD] } |||  '?c} }}S|j |g} ||j|dvrE|j|d|D]-} | | d|k7r| j|j| /| Scc} }}w)aLoad all synsets with a given lemma and part of speech tag. If no pos is specified, all synsets for all parts of speech will be loaded. If lang is specified, all the synsets associated with the lemma name of that language will be returned. r{rru) r#rrPOS_LIST_morphyrErrrr) r$rrrcheck_exceptionsrrpformr synset_listls rr,zWordNetCorpusReader.synsetss(  5=88J..E{ LL3CD#Dkooa4 1f%%%    &K-a00.q1%86A1R5C< &&tzz!}56 sAC1c|j}|dk(rX|j||Dcgc]:}|jD]%}|jj|k(r|'<c}}S|j |g}|j||}|D]b}||j |k7r|j|D]5}|jj|k(s%|j |7d|Scc}}w)zReturn all Lemma objects with a name matching the specified lemma name and part of speech tag. Matches any part of speech tag if none is specified.r{r)r#r,rr~rrr) r$rrrr} lemma_objrsynrs rrzWordNetCorpusReader.lemmass   5=#ll5#6!'>>#))+u4    &F,,u4,0C 1?quuw#~!"t!41I ~~'--/58 i01 1 M#s?C=cH|dk(r-tjSfdjDSj|g}j|dD]1} |dk7r|j j|d|3tt |}|S)zReturn all lemma names for all synsets for the given part of speech tag and language or languages. If pos is not specified, all synsets for all parts of speech will be used.r{c3DK|]}j|vr|ywr_)r)rrrr$s rrz6WordNetCorpusReader.all_lemma_names..!s,d88??s rru)rrrrrr)r$rrrrs`` rall_lemma_namesz#WordNetCorpusReader.all_lemma_namess 5={D6677!%!;!;   &E__T*1- :?qu| T__T215a89 : U$ELrc#K||jvry|j||j|dD]%}|r |d|k(s|j|}|s"|'yw)Nrru)rrrr)r$rrrrs rall_omw_synsetsz#WordNetCorpusReader.all_omw_synsets1sd tzz| # T"//$'* B"R&C-ZZ^H  sAA"A"A"cV|dk(r|j|S|j||S)zIterate over all synsets with a given part of speech tag. If no pos is specified, all synsets for all parts of speech will be loaded. r{)r)rr)all_eng_synsetsru)r$rrs rrzWordNetCorpusReader.all_synsets?s5 5=''C'0 0''Cd'; ;rc#K||jj}n|g}|j}|j}|D]}|tk(rt }n|}d|j|z}|j |} |j} |j} | r| djsO| ||vr ||| } n||| } | ||| <|tk(r| jtk(r| n |tk7r| |j} |j} | r|jy#|jxYww)Nr4r) rrrr9rrrtellr7isspacerclose) r$rpos_tagscachefrom_pos_and_linepos_tagpos_filerr:rrr}s rrwz#WordNetCorpusReader.all_eng_synsetsIsP ;}}))+HuH)) :: + "G '!"x!88F &)I "") ))+7??,!U7^3%*7^F%;F&7w%EF5;E'N62 #g-&++2H"(L%/"(L&^^-F$--/D)6!W+ "N !sA3E6B&D0E0EEc&|j|S)z4return lemmas of the given language as list of wordsr)rsrs rwordszWordNetCorpusReader.wordss###..rc |j||Dcgc]3}ttt|j ||hz 5c}Scc}w)zZreturn nested list with the synonyms of the different senses of word in the given languager)r,r(rrr)r$wordrrs rsynonymszWordNetCorpusReader.synonymssRll4dl3  4BNNN56$?@ A   s8Acd|dk(r|}nL|j}||jvr.tjj |j ||} |j |5}|jcdddS#1swYyxYw#||jvr d|d|cYSd|dcYSxYw)zxReturn the contents of readme, license or citation file use lang=lang to get the file for an individual languager{NzCannot determine z for z Language z is not supported.) rrrr?joinrrreadr)r$filerrrs rdoczWordNetCorpusReader.docs 5=F%%Ftzz|#'',,t'7'7'=dCD <T" !bwwy ! ! ! <t&*4&dV<<"4&(:;;s0B 'B7 B B B B B/'B/c(|jd|S)zlReturn the contents of LICENSE (for omw) use lang=lang to get the license for an individual languageLICENSErrrrs rlicensezWordNetCorpusReader.licensesxxYTx22rc(|jd|S)zjReturn the contents of README (for omw) use lang=lang to get the readme for an individual languageREADMErrrs rreadmezWordNetCorpusReader.readmesxxXDx11rc(|jd|S)zwReturn the contents of citation.bib file (for omw) use lang=lang to get the citation for an individual languagez citation.bibrrrs rcitationzWordNetCorpusReader.citationsxx^$x77rc|jdk7ry|j|jd|_t|j|j}|rt |j dddSy)rr{rrrrru)rwrrr$rxrr-)r$rrs rrzWordNetCorpusReader.lemma_countsg ;;%     '#'99]#;D "4#7#7D t{{3*2./ /rFc(|j|||Sr_rQr$synset1synset2rPr*s rrQz#WordNetCorpusReader.path_similaritys&&wGGrc(|j|||Sr_rYrs rrYz"WordNetCorpusReader.lch_similarity%%gw FFrc(|j|||Sr_r_rs rr_z"WordNetCorpusReader.wup_similarityrrc(|j|||Sr_rhr$rrrdrPs rrhz"WordNetCorpusReader.res_similarity%%gr7;;rc(|j|||Sr_rmrs rrmz"WordNetCorpusReader.jcn_similarityrrc(|j|||Sr_rors rroz"WordNetCorpusReader.lin_similarityrrc\|r|gntD]}|j|||}|s|dcSy)a Find a possible base form for the given form, with the given part of speech, by checking WordNet's list of exceptional forms, or by substituting suffixes for this part of speech. If pos=None, try every part of speech until finding lemmas. Return the first form found in WordNet, or eventually None. >>> from nltk.corpus import wordnet as wn >>> print(wn.morphy('dogs')) dog >>> print(wn.morphy('churches')) church >>> print(wn.morphy('aardwolves')) aardwolf >>> print(wn.morphy('abaci')) abacus >>> wn.morphy('hardrock', wn.ADV) >>> print(wn.morphy('book', wn.NOUN)) book >>> wn.morphy('book', wn.ADJ) rN)rgrh)r$rkrrianalysess rmorphyzWordNetCorpusReader.morphys9, C5X #C||D#/?@H{"  #r) rr)sesr)vesf)xesx)zesz)chesch)shessh)menmaniesy)rr)esr )rr)edr )rr)ingr )rr))err)estr)rr )rr cj}jfd}fd}|r ||vr||}n ||g}||g|zS)Nc |Dcgc]/}D](\}}|j|r|dt| |z*1c}}}Scc}}}wr_)endswithr)formsrkoldnew substitutionss r apply_rulesz0WordNetCorpusReader._morphy..apply_rules"sa" -C==%[Cy!C'' s4Acg}t}|D]J}|jvsj|vs$||vs)|j||j|L|Sr_)rrrr)rrrrkrr$s r filter_formsz1WordNetCorpusReader._morphy..filter_forms*seF5D +4555d88>>t+"MM$/ HHTN  + Mr)rMORPHOLOGICAL_SUBSTITUTIONS) r$rkrri exceptionsrrrrs ` ` @rrhzWordNetCorpusReader._morphysf((- 88=     2t$E 'ETFUN++rct}|jD]}||xxdz cc<i}tD]}tt||<|dkDrUtD] }|||d< |j D]/}|j } | tk(rt} ||| |j<1|D]}|j|} t| dk(r#t ||} |s| t t| z} | D]c}|j } | tk(rt} |jD]#} | D]} || | jxx| z cc<%|| dxx| z cc<e|S)a Creates an information content lookup dictionary from a corpus. :type corpus: CorpusReader :param corpus: The corpus from which we create an information content dictionary. :type weight_senses_equally: bool :param weight_senses_equally: If this is True, gives all possible senses equal weight rather than dividing by the number of possible senses. (If a word has 3 synses, each sense gets 0.3333 per appearance when this is False, 1.0 when it is true.) :param smoothing: How much do we smooth synset counts (default is 1.0) :type smoothing: float :return: An information content dictionary rgr) r rrgrrCrrrrrr,rr)r$rweight_senses_equally smoothingrwwrdpprrpossible_synsetsweightlevelhhs rrdzWordNetCorpusReader.icBs",,. B 2J!OJ  (B 'BrF ( s? &%2q  &&&( 0gg'>C&/3 #  0  %B#||B/ #$)6":&F(%$4 566& %gg'>C4466E#63 +v5+663 f$  % %& rc *|jdd}t|dk7r tdttttttttg|j |<|j D] }t|tr|jd}|jdr7|jjd}t|dkre|dd \}}|d }|jro||jvr|j|}n||jd vr?|jd d |jd vrtj |d|d|d|d d k(r7|j#|} | r$| j%d k(r|j'| }|jd} | d } t| dk(s | d|k(sZ| dk(rX|jjdd}|j |d|j)} || vr| j+|| |j,vs|j ||j,j/| |} || vs| j+|y)a Reads a custom tab file containing mappings of lemmas in the given language to Princeton WordNet 3.0 synset offsets, allowing NLTK's WordNet functions to then be used with that language. See the "Tab files" section at https://omwn.org/omw1.html for documentation on the Multilingual WordNet tab file format. :param tab_file: Tab file as a file or file-like object :type: lang str :param: lang ISO 639-3 code of the language of the tab file rrrz-lang should be a (3 character) ISO 639-3 codezutf-8# Nrjruwordnetrrz: invalid offset z in ''rrrr)rrr)rrr readlines isinstancebytesdecoderrrrreplacerrrrrr#rrr)r$tab_filerlgrtriple offset_poslabelvalwnsspairattr lang_offsets lang_lemmass rrz!WordNetCorpusReader.custom_lemmaszsaZZ_Q  r7a<LM M         !  &&(, 4D$&{{7+??3'++D1v;?$*2AJ! ERj::!TZZ/%)ZZ %; 'djj.CC * 2 23 <#'::i#8!9%MM#'&(9*U4&PQ R!^s*::j1D c 1%)ZZ%5 {{3'Bxt9>T!W]w!iik11#s;'+t's akkmrruc ddlm}m} tt|s t }|s t }fd} |D]c} t | } | t k(rj| )| tk(r | | ;| tk(sE|j| |D] } | |  eD]}j|||||| tt||}|S)ai Produce a graphical representation from 'inputs' (a list of start nodes, which can be a mix of Synsets, Lemmas and/or words), and a synset relation, for drawing with the 'dot' graph visualisation program from the Graphviz package. Return a string in the DOT graph file language, which can then be converted to an image by nltk.parse.dependencygraph.dot2img(dot_string). Optional Parameters: :rel: Wordnet synset relation :pos: for words, restricts Part of Speech to 'n', 'v', 'a' or 'r' :maxdepth: limit the longest path :shapes: dictionary of strings that trigger a specified shape :attr: dictionary with global graph attributes :verbose: warn about cycles >>> from nltk.corpus import wordnet as wn >>> print(wn.digraph([wn.synset('dog.n.01')])) digraph G { "Synset('animal.n.01')" -> "Synset('organism.n.01')"; "Synset('canine.n.02')" -> "Synset('carnivore.n.01')"; "Synset('carnivore.n.01')" -> "Synset('placental.n.01')"; "Synset('chordate.n.01')" -> "Synset('animal.n.01')"; "Synset('dog.n.01')" -> "Synset('canine.n.02')"; "Synset('dog.n.01')" -> "Synset('domestic_animal.n.01')"; "Synset('domestic_animal.n.01')" -> "Synset('animal.n.01')"; "Synset('living_thing.n.01')" -> "Synset('whole.n.02')"; "Synset('mammal.n.01')" -> "Synset('vertebrate.n.01')"; "Synset('object.n.01')" -> "Synset('physical_entity.n.01')"; "Synset('organism.n.01')" -> "Synset('living_thing.n.01')"; "Synset('physical_entity.n.01')" -> "Synset('entity.n.01')"; "Synset('placental.n.01')" -> "Synset('mammal.n.01')"; "Synset('vertebrate.n.01')" -> "Synset('chordate.n.01')"; "Synset('whole.n.02')" -> "Synset('object.n.01')"; } r) edge_closure edges2dotcn|j}j|j||fyr_)r}r)rredgesr,s r add_lemmaz.WordNetCorpusReader.digraph..add_lemma s)B KKO IIsBi r)shapesr)rrrrrrrrrnrrunionr(r)r$inputsrrmaxdepthrrrPrrrnodetyprr dot_stringrr,s @@rdigraphzWordNetCorpusReader.digraphs` 6%VF6D !  %Dt*Cf} D!$![[s3%Ee$% % JBKK Rh HIE Jvd5k26M rr_)rrt)Nr{Trz)NN)rr{rwrv)NT)T)FrO)GrrrrrrrADVrVERBrrKrrDrrrrrrrrrrrrrrrrrrrVrrr(r}rrr r@r9rer,rrsrurrwrrrrrrrrQrrYr_rhrmrorrrhrdrrr __classcell__)rs@rr{r{WsvI%<!C#tTUCfdFCH!T1c1c1gqAL@<+=+=+?@@J F$B6H$ 4,$ $D3 )8 .6+ -/Tb@ % JB6@ (BHI@J@wr&7X>44<5"n/ <$3 2 8 H%44<. sH-a4Hsz> LCS Subsumer by content:)rrrrrrr)rrrdrPrerfr[ subsumer_ics ` rrbrbq s"||w||#Information content file has no entries for part-of-speech: %sr) rrrr.rrrkrFrW)r}rdricposr msgrs rrr s ++C g~-3 6>> "F { %(*+++ -N39%1,-sA A7!A22A7cZ|ddk(rtS|ddk(rtSd|z}t|)NrurrzLUnidentified part of speech in WordNet Information Content file for field %s)rrr))fieldrs rrr sB RyC rc   " # orrwrv)2rrFrrr collectionsrr functoolsr itertoolsrroperatorrnltk.corpus.readerr nltk.internalsr nltk.probabilityr rr r$rkrrrrrrgrIcompiler Exceptionrrrnrr{rrQrYr_rhrmrorbrrrrrr&s"  *$#+%%?( !8Wc4 $S !*Xbjj& 999U(U(U(pA#NA#Hp ^p p{,{F&'L'j YY@@@!0088..66..66..66..66..66"!P,* r