119 lines
4 KiB
Python
119 lines
4 KiB
Python
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# Natural Language Toolkit: Cooper storage for Quantifier Ambiguity
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#
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# Copyright (C) 2001-2018 NLTK Project
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# Author: Ewan Klein <ewan@inf.ed.ac.uk>
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# URL: <http://nltk.org/>
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# For license information, see LICENSE.TXT
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from __future__ import print_function
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from nltk.sem.logic import LambdaExpression, ApplicationExpression, Variable
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from nltk.parse import load_parser
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from nltk.parse.featurechart import InstantiateVarsChart
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class CooperStore(object):
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"""
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A container for handling quantifier ambiguity via Cooper storage.
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"""
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def __init__(self, featstruct):
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"""
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:param featstruct: The value of the ``sem`` node in a tree from
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``parse_with_bindops()``
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:type featstruct: FeatStruct (with features ``core`` and ``store``)
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"""
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self.featstruct = featstruct
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self.readings = []
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try:
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self.core = featstruct['CORE']
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self.store = featstruct['STORE']
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except KeyError:
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print("%s is not a Cooper storage structure" % featstruct)
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def _permute(self, lst):
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"""
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:return: An iterator over the permutations of the input list
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:type lst: list
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:rtype: iter
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"""
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remove = lambda lst0, index: lst0[:index] + lst0[index+1:]
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if lst:
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for index, x in enumerate(lst):
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for y in self._permute(remove(lst, index)):
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yield (x,)+y
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else: yield ()
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def s_retrieve(self, trace=False):
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"""
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Carry out S-Retrieval of binding operators in store. If hack=True,
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serialize the bindop and core as strings and reparse. Ugh.
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Each permutation of the store (i.e. list of binding operators) is
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taken to be a possible scoping of quantifiers. We iterate through the
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binding operators in each permutation, and successively apply them to
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the current term, starting with the core semantic representation,
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working from the inside out.
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Binding operators are of the form::
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bo(\P.all x.(man(x) -> P(x)),z1)
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"""
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for perm, store_perm in enumerate(self._permute(self.store)):
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if trace:
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print("Permutation %s" % (perm+1))
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term = self.core
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for bindop in store_perm:
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# we just want the arguments that are wrapped by the 'bo' predicate
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quant, varex = tuple(bindop.args)
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# use var to make an abstraction over the current term and then
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# apply the quantifier to it
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term = ApplicationExpression(quant, LambdaExpression(varex.variable, term))
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if trace:
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print(" ", term)
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term = term.simplify()
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self.readings.append(term)
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def parse_with_bindops(sentence, grammar=None, trace=0):
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"""
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Use a grammar with Binding Operators to parse a sentence.
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"""
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if not grammar:
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grammar = 'grammars/book_grammars/storage.fcfg'
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parser = load_parser(grammar, trace=trace, chart_class=InstantiateVarsChart)
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# Parse the sentence.
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tokens = sentence.split()
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return list(parser.parse(tokens))
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def demo():
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from nltk.sem import cooper_storage as cs
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sentence = "every girl chases a dog"
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#sentence = "a man gives a bone to every dog"
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print()
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print("Analyis of sentence '%s'" % sentence)
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print("=" * 50)
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trees = cs.parse_with_bindops(sentence, trace=0)
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for tree in trees:
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semrep = cs.CooperStore(tree.label()['SEM'])
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print()
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print("Binding operators:")
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print("-" * 15)
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for s in semrep.store:
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print(s)
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print()
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print("Core:")
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print("-" * 15)
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print(semrep.core)
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print()
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print("S-Retrieval:")
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print("-" * 15)
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semrep.s_retrieve(trace=True)
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print("Readings:")
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print("-" * 15)
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for i, reading in enumerate(semrep.readings):
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print("%s: %s" % (i+1, reading))
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if __name__ == '__main__':
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demo()
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