NLTK10《Python自然语言处理》code09 建立基于特征的文法
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建立基于特征的文法
# -*- coding: utf-8 -*-# win10 python3.5.3/python3.6.1 nltk3.2.4# 《Python自然语言处理》 09 建立基于特征的文法# pnlp09.pyimport nltk# 9.1 文法特征kim = {'CAT': 'NP', 'ORTH': 'Kim', 'REF': 'k'}chase = {'CAT': 'V', 'ORTH': 'chased', 'REL': 'chase'}# 对象kim、chase有一些共同特征，CAT(文法类别)、ORTH(正字法，即拼写)# 具有面向语义的特征：kim['REF']表示kim的指示物，chase['REL']表示chase表示的关系chase['AGT'] = 'sbj' # sbj：主语chase['PAT'] = 'obj' # obj:宾语sent = "Kim chased Lee"tokens = sent.split()lee = {'CAT': 'NP', 'ORTH': 'Lee', 'REF': 'l'}def lex2fs(word): for fs in [kim, lee, chase]: if fs['ORTH'] ==word: return fssubj, verb, obj = lex2fs(tokens[0]), lex2fs(tokens[1]), lex2fs(tokens[2])verb['AGT'] = subj['REF'] # agent of 'chase' is Kimverb['PAT'] = obj['REF'] # patient of 'chase' is Leefor k in ['ORTH', 'REL', 'AGT', 'PAT']: # check featstruct of 'chase' print("%-5s => %s" % (k, verb[k]))"""ORTH => chasedREL => chaseAGT => kPAT => l"""surprise = {'CAT': 'V', 'ORTH': 'surprised', 'REL': 'surprise', 'SRC': 'sbj', 'EXP': 'obj'}# 句法协议# 使用属性和约束# 例9-1 基于特征的文法例子nltk.data.show_cfg('grammars/book_grammars/feat0.fcfg')"""% start S# #################### Grammar Productions# #################### S expansion productionsS -> NP[NUM=?n] VP[NUM=?n]# NP expansion productionsNP[NUM=?n] -> N[NUM=?n] NP[NUM=?n] -> PropN[NUM=?n] NP[NUM=?n] -> Det[NUM=?n] N[NUM=?n]NP[NUM=pl] -> N[NUM=pl] # VP expansion productionsVP[TENSE=?t, NUM=?n] -> IV[TENSE=?t, NUM=?n]VP[TENSE=?t, NUM=?n] -> TV[TENSE=?t, NUM=?n] NP# #################### Lexical Productions# ###################Det[NUM=sg] -> 'this' | 'every'Det[NUM=pl] -> 'these' | 'all'Det -> 'the' | 'some' | 'several'PropN[NUM=sg]-> 'Kim' | 'Jody'N[NUM=sg] -> 'dog' | 'girl' | 'car' | 'child'N[NUM=pl] -> 'dogs' | 'girls' | 'cars' | 'children' IV[TENSE=pres, NUM=sg] -> 'disappears' | 'walks'TV[TENSE=pres, NUM=sg] -> 'sees' | 'likes'IV[TENSE=pres, NUM=pl] -> 'disappear' | 'walk'TV[TENSE=pres, NUM=pl] -> 'see' | 'like'IV[TENSE=past] -> 'disappeared' | 'walked'TV[TENSE=past] -> 'saw' | 'liked'"""# 例9-2 跟踪基于特征的图表分析器tokens = 'Kim likes children'.split()from nltk import load_parsercp = load_parser('grammars/book_grammars/feat0.fcfg', trace=2)trees = cp.parse(tokens)"""|.Kim .like.chil.|Leaf Init Rule:|[----] . .| [0:1] 'Kim'|. [----] .| [1:2] 'likes'|. . [----]| [2:3] 'children'Feature Bottom Up Predict Combine Rule:|[----] . .| [0:1] PropN[NUM='sg'] -> 'Kim' *Feature Bottom Up Predict Combine Rule:|[----] . .| [0:1] NP[NUM='sg'] -> PropN[NUM='sg'] *Feature Bottom Up Predict Combine Rule:|[----> . .| [0:1] S[] -> NP[NUM=?n] * VP[NUM=?n] {?n: 'sg'}Feature Bottom Up Predict Combine Rule:|. [----] .| [1:2] TV[NUM='sg', TENSE='pres'] -> 'likes' *Feature Bottom Up Predict Combine Rule:|. [----> .| [1:2] VP[NUM=?n, TENSE=?t] -> TV[NUM=?n, TENSE=?t] * NP[] {?n: 'sg', ?t: 'pres'}Feature Bottom Up Predict Combine Rule:|. . [----]| [2:3] N[NUM='pl'] -> 'children' *Feature Bottom Up Predict Combine Rule:|. . [----]| [2:3] NP[NUM='pl'] -> N[NUM='pl'] *Feature Bottom Up Predict Combine Rule:|. . [---->| [2:3] S[] -> NP[NUM=?n] * VP[NUM=?n] {?n: 'pl'}Feature Single Edge Fundamental Rule:|. [---------]| [1:3] VP[NUM='sg', TENSE='pres'] -> TV[NUM='sg', TENSE='pres'] NP[] *Feature Single Edge Fundamental Rule:|[==============]| [0:3] S[] -> NP[NUM='sg'] VP[NUM='sg'] *"""for tree in trees:print(tree)"""(S[] (NP[NUM='sg'] (PropN[NUM='sg'] Kim)) (VP[NUM='sg', TENSE='pres'] (TV[NUM='sg', TENSE='pres'] likes) (NP[NUM='pl'] (N[NUM='pl'] children))))"""# 术语# 9.2 处理特征结构fs1 = nltk.FeatStruct(TENSE='past', NUM='sg')print(fs1)"""[ NUM = 'sg' ][ TENSE = 'past' ]"""fs1 = nltk.FeatStruct(PER=3, NUM='pl', GND='fem')print(fs1['GND']) # femfs1['CASE'] = 'acc'fs2 = nltk.FeatStruct(POS='N', AGR=fs1)print(fs2)"""[ [ CASE = 'acc' ] ][ AGR = [ GND = 'fem' ] ][ [ NUM = 'pl' ] ][ [ PER = 3 ] ][ ][ POS = 'N' ]"""print(fs2['AGR'])"""[ CASE = 'acc' ][ GND = 'fem' ][ NUM = 'pl' ][ PER = 3 ]"""print(fs2['AGR']['PER']) # 3print(nltk.FeatStruct("[POS='N', AGR=[PER=3, NUM='pl', GND='fem']]"))"""[ [ GND = 'fem' ] ][ AGR = [ NUM = 'pl' ] ][ [ PER = 3 ] ][ ][ POS = 'N' ]"""print(nltk.FeatStruct(name='Lee', telno='01 27 86 42 96', age=33))"""[ age = 33 ][ name = 'Lee' ][ telno = '01 27 86 42 96' ]"""print(nltk.FeatStruct("""[NAME='Lee', ADDRESS=(1)[NUMBER=74, STREET='rue Pascal'],SPOUSE=[NAME='Kim', ADDRESS->(1)]]"""))"""[ ADDRESS = (1) [ NUMBER = 74 ] ][ [ STREET = 'rue Pascal' ] ][ ][ NAME = 'Lee' ][ ][ SPOUSE = [ ADDRESS -> (1) ] ][ [ NAME = 'Kim' ] ]"""print(nltk.FeatStruct("[A='a', B=(1)[C='c'], D->(1), E->(1)]"))"""[ A = 'a' ][ ][ B = (1) [ C = 'c' ] ][ ][ D -> (1) ][ E -> (1) ]"""# 包含和统一fs1 = nltk.FeatStruct(NUMBER=74, STREET='rue Pascal')fs2 = nltk.FeatStruct(CITY='Paris')print(fs1.unify(fs2))"""[ CITY = 'Paris' ][ NUMBER = 74 ][ STREET = 'rue Pascal' ]"""print(fs2.unify(fs1))"""[ CITY = 'Paris' ][ NUMBER = 74 ][ STREET = 'rue Pascal' ]"""fs0 = nltk.FeatStruct(A='a')fs1 = nltk.FeatStruct(A='b')fs2 = fs0.unify(fs1)print(fs2) # Nonefs0 = nltk.FeatStruct("""[NAME=Lee,ADDRESS=[NUMBER=74,STREET='rue Pascal'],SPOUSE=[NAME=Kim, ADDRESS=[number=74,STREET='rue Pascal']]]""")print(fs0)"""[ ADDRESS = [ NUMBER = 74 ] ][ [ STREET = 'rue Pascal' ] ][ ][ NAME = 'Lee' ][ ][ [ ADDRESS = [ STREET = 'rue Pascal' ] ] ][ SPOUSE = [ [ number = 74 ] ] ][ [ ] ][ [ NAME = 'Kim' ] ]"""fs1 = nltk.FeatStruct("[SPOUSE=[ADDRESS=[CITY=Paris]]]")print(fs1.unify(fs0))"""[ ADDRESS = [ NUMBER = 74 ] ][ [ STREET = 'rue Pascal' ] ][ ][ NAME = 'Lee' ][ ][ [ [ CITY = 'Paris' ] ] ][ [ ADDRESS = [ STREET = 'rue Pascal' ] ] ][ SPOUSE = [ [ number = 74 ] ] ][ [ ] ][ [ NAME = 'Kim' ] ]"""fs2 = nltk.FeatStruct("""[NAME=Lee, ADDRESS=(1)[NUMBER=74, STREET='rue Pascal'],SPOUSE=[NAME=Kim, ADDRESS->(1)]]""")print(fs1.unify(fs2))"""[ ADDRESS = (1) [ NUMBER = 74 ] ][ [ STREET = 'rue Pascal' ] ][ ][ NAME = 'Lee' ][ ][ SPOUSE = [ ADDRESS -> (1) ] ][ [ NAME = 'Kim' ] ]"""fs1 = nltk.FeatStruct("[ADDRESS1=[NUMBER=74, STREET='rue Pascal']]")fs2 = nltk.FeatStruct("[ADDRESS1=?x, ADDRESS2=?x]")print(fs2)"""[ ADDRESS1 = ?x ][ ADDRESS2 = ?x ]"""print(fs2.unify(fs1))"""[ ADDRESS1 = (1) [ NUMBER = 74 ] ][ [ STREET = 'rue Pascal' ] ][ ][ ADDRESS2 -> (1) ]"""# 9.3 扩展基于特征的文法# 子类别# 核心词# 助动词和倒装# 无限制依赖成分# 例9-3 具有倒装从句和长距离依赖的产生式的文法，使用斜线类别nltk.data.show_cfg('grammars/book_grammars/feat1.fcfg')"""% start S# #################### Grammar Productions# ###################S[-INV] -> NP VPS[-INV]/?x -> NP VP/?xS[-INV] -> NP S/NPS[-INV] -> Adv[+NEG] S[+INV]S[+INV] -> V[+AUX] NP VPS[+INV]/?x -> V[+AUX] NP VP/?xSBar -> Comp S[-INV]SBar/?x -> Comp S[-INV]/?xVP -> V[SUBCAT=intrans, -AUX]VP -> V[SUBCAT=trans, -AUX] NPVP/?x -> V[SUBCAT=trans, -AUX] NP/?xVP -> V[SUBCAT=clause, -AUX] SBarVP/?x -> V[SUBCAT=clause, -AUX] SBar/?xVP -> V[+AUX] VPVP/?x -> V[+AUX] VP/?x# #################### Lexical Productions# ###################V[SUBCAT=intrans, -AUX] -> 'walk' | 'sing'V[SUBCAT=trans, -AUX] -> 'see' | 'like'V[SUBCAT=clause, -AUX] -> 'say' | 'claim'V[+AUX] -> 'do' | 'can'NP[-WH] -> 'you' | 'cats'NP[+WH] -> 'who'Adv[+NEG] -> 'rarely' | 'never'NP/NP ->Comp -> 'that'"""tokens = 'who do you claim that you like'.split()from nltk import load_parsercp = load_parser('grammars/book_grammars/feat1.fcfg')for tree in cp.parse(tokens): print(tree)"""(S[-INV] (NP[+WH] who) (S[+INV]/NP[] (V[+AUX] do) (NP[-WH] you) (VP[]/NP[] (V[-AUX, SUBCAT='clause'] claim) (SBar[]/NP[] (Comp[] that) (S[-INV]/NP[] (NP[-WH] you) (VP[]/NP[] (V[-AUX, SUBCAT='trans'] like) (NP[]/NP[] )))))))"""tokens = 'you claim that you like cats'.split()for tree in cp.parse(tokens): print(tree)"""(S[-INV] (NP[-WH] you) (VP[] (V[-AUX, SUBCAT='clause'] claim) (SBar[] (Comp[] that) (S[-INV] (NP[-WH] you) (VP[] (V[-AUX, SUBCAT='trans'] like) (NP[-WH] cats))))))"""tokens = 'rarely do you sing'.split()for tree in cp.parse(tokens): print(tree)"""(S[-INV] (Adv[+NEG] rarely) (S[+INV] (V[+AUX] do) (NP[-WH] you) (VP[] (V[-AUX, SUBCAT='intrans'] sing))))"""# 例9-4 基于特征的文法的例子nltk.data.show_cfg('grammars/book_grammars/german.fcfg')"""% start S# Grammar ProductionsS -> NP[CASE=nom, AGR=?a] VP[AGR=?a]NP[CASE=?c, AGR=?a] -> PRO[CASE=?c, AGR=?a]NP[CASE=?c, AGR=?a] -> Det[CASE=?c, AGR=?a] N[CASE=?c, AGR=?a]..."""tokens = 'ich folge den Katzen'.split()cp = nltk.load_parser('grammars/book_grammars/german.fcfg')for tree in cp.parse(tokens): print(tree)"""(S[] (NP[AGR=[NUM='sg', PER=1], CASE='nom'] (PRO[AGR=[NUM='sg', PER=1], CASE='nom'] ich)) (VP[AGR=[NUM='sg', PER=1]] (TV[AGR=[NUM='sg', PER=1], OBJCASE='dat'] folge) (NP[AGR=[GND='fem', NUM='pl', PER=3], CASE='dat'] (Det[AGR=[NUM='pl', PER=3], CASE='dat'] den) (N[AGR=[GND='fem', NUM='pl', PER=3]] Katzen))))"""tokens = 'ich folge den Katze'.split()cp = nltk.load_parser('grammars/book_grammars/german.fcfg', trace=2)for tree in cp.parse(tokens): print(tree)"""|.ich.fol.den.Kat.|Leaf Init Rule:|[---] . . .| [0:1] 'ich'|. [---] . .| [1:2] 'folge'|. . [---] .| [2:3] 'den'|. . . [---]| [3:4] 'Katze'Feature Bottom Up Predict Combine Rule:|[---] . . .| [0:1] PRO[AGR=[NUM='sg', PER=1], CASE='nom'] -> 'ich' *Feature Bottom Up Predict Combine Rule:|[---] . . .| [0:1] NP[AGR=[NUM='sg', PER=1], CASE='nom'] -> PRO[AGR=[NUM='sg', PER=1], CASE='nom'] *Feature Bottom Up Predict Combine Rule:|[---> . . .| [0:1] S[] -> NP[AGR=?a, CASE='nom'] * VP[AGR=?a] {?a: [NUM='sg', PER=1]}Feature Bottom Up Predict Combine Rule:|. [---] . .| [1:2] TV[AGR=[NUM='sg', PER=1], OBJCASE='dat'] -> 'folge' *Feature Bottom Up Predict Combine Rule:|. [---> . .| [1:2] VP[AGR=?a] -> TV[AGR=?a, OBJCASE=?c] * NP[CASE=?c] {?a: [NUM='sg', PER=1], ?c: 'dat'}Feature Bottom Up Predict Combine Rule:|. . [---] .| [2:3] Det[AGR=[GND='masc', NUM='sg', PER=3], CASE='acc'] -> 'den' *|. . [---] .| [2:3] Det[AGR=[NUM='pl', PER=3], CASE='dat'] -> 'den' *Feature Bottom Up Predict Combine Rule:|. . [---> .| [2:3] NP[AGR=?a, CASE=?c] -> Det[AGR=?a, CASE=?c] * N[AGR=?a, CASE=?c] {?a: [NUM='pl', PER=3], ?c: 'dat'}Feature Bottom Up Predict Combine Rule:|. . [---> .| [2:3] NP[AGR=?a, CASE=?c] -> Det[AGR=?a, CASE=?c] * N[AGR=?a, CASE=?c] {?a: [GND='masc', NUM='sg', PER=3], ?c: 'acc'}Feature Bottom Up Predict Combine Rule:|. . . [---]| [3:4] N[AGR=[GND='fem', NUM='sg', PER=3]] -> 'Katze' *"""阅读全文
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