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377 lines
19 KiB
Plaintext
377 lines
19 KiB
Plaintext
5 years ago
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.. Copyright (C) 2001-2019 NLTK Project
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.. For license information, see LICENSE.TXT
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==============================
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Combinatory Categorial Grammar
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==============================
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Relative Clauses
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----------------
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>>> from nltk.ccg import chart, lexicon
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Construct a lexicon:
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>>> lex = lexicon.parseLexicon('''
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... :- S, NP, N, VP
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...
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... Det :: NP/N
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... Pro :: NP
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... Modal :: S\\NP/VP
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...
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... TV :: VP/NP
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... DTV :: TV/NP
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...
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... the => Det
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...
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... that => Det
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... that => NP
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...
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... I => Pro
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... you => Pro
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... we => Pro
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...
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... chef => N
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... cake => N
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... children => N
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... dough => N
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...
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... will => Modal
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... should => Modal
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... might => Modal
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... must => Modal
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...
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... and => var\\.,var/.,var
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...
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... to => VP[to]/VP
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...
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... without => (VP\\VP)/VP[ing]
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...
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... be => TV
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... cook => TV
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... eat => TV
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...
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... cooking => VP[ing]/NP
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...
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... give => DTV
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...
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... is => (S\\NP)/NP
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... prefer => (S\\NP)/NP
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...
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... which => (N\\N)/(S/NP)
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...
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... persuade => (VP/VP[to])/NP
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... ''')
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>>> parser = chart.CCGChartParser(lex, chart.DefaultRuleSet)
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>>> for parse in parser.parse("you prefer that cake".split()):
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... chart.printCCGDerivation(parse)
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... break
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...
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you prefer that cake
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NP ((S\NP)/NP) (NP/N) N
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-------------->
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NP
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--------------------------->
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(S\NP)
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--------------------------------<
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S
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>>> for parse in parser.parse("that is the cake which you prefer".split()):
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... chart.printCCGDerivation(parse)
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... break
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...
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that is the cake which you prefer
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NP ((S\NP)/NP) (NP/N) N ((N\N)/(S/NP)) NP ((S\NP)/NP)
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----->T
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(S/(S\NP))
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------------------>B
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(S/NP)
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---------------------------------->
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(N\N)
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----------------------------------------<
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N
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------------------------------------------------>
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NP
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------------------------------------------------------------->
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(S\NP)
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-------------------------------------------------------------------<
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S
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Some other sentences to try:
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"that is the cake which we will persuade the chef to cook"
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"that is the cake which we will persuade the chef to give the children"
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>>> sent = "that is the dough which you will eat without cooking".split()
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>>> nosub_parser = chart.CCGChartParser(lex, chart.ApplicationRuleSet +
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... chart.CompositionRuleSet + chart.TypeRaiseRuleSet)
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Without Substitution (no output)
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>>> for parse in nosub_parser.parse(sent):
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... chart.printCCGDerivation(parse)
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With Substitution:
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>>> for parse in parser.parse(sent):
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... chart.printCCGDerivation(parse)
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... break
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...
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that is the dough which you will eat without cooking
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NP ((S\NP)/NP) (NP/N) N ((N\N)/(S/NP)) NP ((S\NP)/VP) (VP/NP) ((VP\VP)/VP['ing']) (VP['ing']/NP)
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----->T
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(S/(S\NP))
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------------------------------------->B
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((VP\VP)/NP)
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----------------------------------------------<Sx
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(VP/NP)
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----------------------------------------------------------->B
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((S\NP)/NP)
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---------------------------------------------------------------->B
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(S/NP)
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-------------------------------------------------------------------------------->
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(N\N)
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---------------------------------------------------------------------------------------<
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N
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----------------------------------------------------------------------------------------------->
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NP
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------------------------------------------------------------------------------------------------------------>
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(S\NP)
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------------------------------------------------------------------------------------------------------------------<
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S
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Conjunction
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-----------
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>>> from nltk.ccg.chart import CCGChartParser, ApplicationRuleSet, CompositionRuleSet
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>>> from nltk.ccg.chart import SubstitutionRuleSet, TypeRaiseRuleSet, printCCGDerivation
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>>> from nltk.ccg import lexicon
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Lexicons for the tests:
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>>> test1_lex = '''
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... :- S,N,NP,VP
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... I => NP
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... you => NP
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... will => S\\NP/VP
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... cook => VP/NP
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... which => (N\\N)/(S/NP)
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... and => var\\.,var/.,var
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... might => S\\NP/VP
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... eat => VP/NP
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... the => NP/N
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... mushrooms => N
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... parsnips => N'''
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>>> test2_lex = '''
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... :- N, S, NP, VP
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... articles => N
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... the => NP/N
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... and => var\\.,var/.,var
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... which => (N\\N)/(S/NP)
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... I => NP
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... anyone => NP
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... will => (S/VP)\\NP
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... file => VP/NP
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... without => (VP\\VP)/VP[ing]
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... forget => VP/NP
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... reading => VP[ing]/NP
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... '''
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Tests handling of conjunctions.
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Note that while the two derivations are different, they are semantically equivalent.
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>>> lex = lexicon.parseLexicon(test1_lex)
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>>> parser = CCGChartParser(lex, ApplicationRuleSet + CompositionRuleSet + SubstitutionRuleSet)
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>>> for parse in parser.parse("I will cook and might eat the mushrooms and parsnips".split()):
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... printCCGDerivation(parse)
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I will cook and might eat the mushrooms and parsnips
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NP ((S\NP)/VP) (VP/NP) ((_var0\.,_var0)/.,_var0) ((S\NP)/VP) (VP/NP) (NP/N) N ((_var0\.,_var0)/.,_var0) N
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---------------------->B
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((S\NP)/NP)
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---------------------->B
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((S\NP)/NP)
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------------------------------------------------->
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(((S\NP)/NP)\.,((S\NP)/NP))
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-----------------------------------------------------------------------<
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((S\NP)/NP)
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------------------------------------->
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(N\.,N)
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------------------------------------------------<
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N
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-------------------------------------------------------->
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NP
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------------------------------------------------------------------------------------------------------------------------------->
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(S\NP)
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-----------------------------------------------------------------------------------------------------------------------------------<
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S
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I will cook and might eat the mushrooms and parsnips
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NP ((S\NP)/VP) (VP/NP) ((_var0\.,_var0)/.,_var0) ((S\NP)/VP) (VP/NP) (NP/N) N ((_var0\.,_var0)/.,_var0) N
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---------------------->B
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((S\NP)/NP)
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---------------------->B
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((S\NP)/NP)
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------------------------------------------------->
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(((S\NP)/NP)\.,((S\NP)/NP))
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-----------------------------------------------------------------------<
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((S\NP)/NP)
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------------------------------------------------------------------------------->B
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((S\NP)/N)
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------------------------------------->
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(N\.,N)
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------------------------------------------------<
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N
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------------------------------------------------------------------------------------------------------------------------------->
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(S\NP)
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-----------------------------------------------------------------------------------------------------------------------------------<
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S
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Tests handling subject extraction.
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Interesting to point that the two parses are clearly semantically different.
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>>> lex = lexicon.parseLexicon(test2_lex)
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>>> parser = CCGChartParser(lex, ApplicationRuleSet + CompositionRuleSet + SubstitutionRuleSet)
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>>> for parse in parser.parse("articles which I will file and forget without reading".split()):
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... printCCGDerivation(parse)
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articles which I will file and forget without reading
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N ((N\N)/(S/NP)) NP ((S/VP)\NP) (VP/NP) ((_var0\.,_var0)/.,_var0) (VP/NP) ((VP\VP)/VP['ing']) (VP['ing']/NP)
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-----------------<
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(S/VP)
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------------------------------------->B
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((VP\VP)/NP)
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----------------------------------------------<Sx
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(VP/NP)
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------------------------------------------------------------------------->
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((VP/NP)\.,(VP/NP))
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----------------------------------------------------------------------------------<
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(VP/NP)
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--------------------------------------------------------------------------------------------------->B
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(S/NP)
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------------------------------------------------------------------------------------------------------------------->
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(N\N)
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-----------------------------------------------------------------------------------------------------------------------------<
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N
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articles which I will file and forget without reading
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N ((N\N)/(S/NP)) NP ((S/VP)\NP) (VP/NP) ((_var0\.,_var0)/.,_var0) (VP/NP) ((VP\VP)/VP['ing']) (VP['ing']/NP)
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-----------------<
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(S/VP)
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------------------------------------>
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((VP/NP)\.,(VP/NP))
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---------------------------------------------<
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(VP/NP)
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------------------------------------->B
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((VP\VP)/NP)
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----------------------------------------------------------------------------------<Sx
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(VP/NP)
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--------------------------------------------------------------------------------------------------->B
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(S/NP)
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------------------------------------------------------------------------------------------------------------------->
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(N\N)
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-----------------------------------------------------------------------------------------------------------------------------<
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N
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Unicode support
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---------------
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Unicode words are supported.
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>>> from nltk.ccg import chart, lexicon
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Lexicons for the tests:
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>>> lex = lexicon.parseLexicon(u'''
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... :- S, N, NP, PP
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...
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... AdjI :: N\\N
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... AdjD :: N/N
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... AdvD :: S/S
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... AdvI :: S\\S
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... Det :: NP/N
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... PrepNPCompl :: PP/NP
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... PrepNAdjN :: S\\S/N
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... PrepNAdjNP :: S\\S/NP
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... VPNP :: S\\NP/NP
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... VPPP :: S\\NP/PP
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... VPser :: S\\NP/AdjI
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...
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... auto => N
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... bebidas => N
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... cine => N
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... ley => N
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... libro => N
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... ministro => N
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... panadería => N
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... presidente => N
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... super => N
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...
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... el => Det
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... la => Det
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... las => Det
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... un => Det
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...
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... Ana => NP
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... Pablo => NP
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...
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... y => var\\.,var/.,var
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...
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... pero => (S/NP)\\(S/NP)/(S/NP)
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...
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... anunció => VPNP
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... compró => VPNP
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... cree => S\\NP/S[dep]
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... desmintió => VPNP
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... lee => VPNP
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... fueron => VPPP
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...
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... es => VPser
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...
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... interesante => AdjD
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... interesante => AdjI
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... nueva => AdjD
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... nueva => AdjI
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...
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... a => PrepNPCompl
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... en => PrepNAdjN
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... en => PrepNAdjNP
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...
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... ayer => AdvI
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...
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... que => (NP\\NP)/(S/NP)
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... que => S[dep]/S
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... ''')
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>>> parser = chart.CCGChartParser(lex, chart.DefaultRuleSet)
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>>> for parse in parser.parse(u"el ministro anunció pero el presidente desmintió la nueva ley".split()):
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... printCCGDerivation(parse) # doctest: +SKIP
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... # it fails on python2.7 because of the unicode problem explained in https://github.com/nltk/nltk/pull/1354
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... break
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el ministro anunció pero el presidente desmintió la nueva ley
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(NP/N) N ((S\NP)/NP) (((S/NP)\(S/NP))/(S/NP)) (NP/N) N ((S\NP)/NP) (NP/N) (N/N) N
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------------------>
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NP
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------------------>T
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(S/(S\NP))
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-------------------->
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NP
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-------------------->T
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(S/(S\NP))
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--------------------------------->B
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(S/NP)
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----------------------------------------------------------->
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((S/NP)\(S/NP))
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------------>
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N
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-------------------->
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NP
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--------------------<T
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(S\(S/NP))
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-------------------------------------------------------------------------------<B
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(S\(S/NP))
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--------------------------------------------------------------------------------------------<B
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(S/NP)
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-------------------------------------------------------------------------------------------------------------->
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S
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