613 lines
22 KiB
Python
613 lines
22 KiB
Python
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# Natural Language Toolkit: Discourse Processing
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#
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# Author: Ewan Klein <ewan@inf.ed.ac.uk>
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# Dan Garrette <dhgarrette@gmail.com>
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#
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# URL: <http://nltk.org/>
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# For license information, see LICENSE.TXT
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"""
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Module for incrementally developing simple discourses, and checking for semantic ambiguity,
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consistency and informativeness.
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Many of the ideas are based on the CURT family of programs of Blackburn and Bos
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(see http://homepages.inf.ed.ac.uk/jbos/comsem/book1.html).
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Consistency checking is carried out by using the ``mace`` module to call the Mace4 model builder.
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Informativeness checking is carried out with a call to ``Prover.prove()`` from
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the ``inference`` module.
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``DiscourseTester`` is a constructor for discourses.
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The basic data structure is a list of sentences, stored as ``self._sentences``. Each sentence in the list
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is assigned a "sentence ID" (``sid``) of the form ``s``\ *i*. For example::
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s0: A boxer walks
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s1: Every boxer chases a girl
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Each sentence can be ambiguous between a number of readings, each of which receives a
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"reading ID" (``rid``) of the form ``s``\ *i* -``r``\ *j*. For example::
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s0 readings:
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s0-r1: some x.(boxer(x) & walk(x))
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s0-r0: some x.(boxerdog(x) & walk(x))
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A "thread" is a list of readings, represented as a list of ``rid``\ s.
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Each thread receives a "thread ID" (``tid``) of the form ``d``\ *i*.
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For example::
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d0: ['s0-r0', 's1-r0']
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The set of all threads for a discourse is the Cartesian product of all the readings of the sequences of sentences.
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(This is not intended to scale beyond very short discourses!) The method ``readings(filter=True)`` will only show
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those threads which are consistent (taking into account any background assumptions).
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"""
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from __future__ import print_function
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from abc import ABCMeta, abstractmethod
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from six import add_metaclass
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import os
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from operator import and_, add
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from functools import reduce
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from nltk.data import show_cfg
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from nltk.tag import RegexpTagger
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from nltk.parse import load_parser
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from nltk.parse.malt import MaltParser
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from nltk.sem.drt import resolve_anaphora, AnaphoraResolutionException
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from nltk.sem.glue import DrtGlue
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from nltk.sem.logic import Expression
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from nltk.inference.mace import MaceCommand
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from nltk.inference.prover9 import Prover9Command
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@add_metaclass(ABCMeta)
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class ReadingCommand(object):
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@abstractmethod
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def parse_to_readings(self, sentence):
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"""
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:param sentence: the sentence to read
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:type sentence: str
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"""
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def process_thread(self, sentence_readings):
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"""
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This method should be used to handle dependencies between readings such
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as resolving anaphora.
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:param sentence_readings: readings to process
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:type sentence_readings: list(Expression)
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:return: the list of readings after processing
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:rtype: list(Expression)
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"""
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return sentence_readings
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@abstractmethod
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def combine_readings(self, readings):
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"""
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:param readings: readings to combine
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:type readings: list(Expression)
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:return: one combined reading
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:rtype: Expression
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"""
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@abstractmethod
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def to_fol(self, expression):
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"""
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Convert this expression into a First-Order Logic expression.
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:param expression: an expression
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:type expression: Expression
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:return: a FOL version of the input expression
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:rtype: Expression
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"""
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class CfgReadingCommand(ReadingCommand):
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def __init__(self, gramfile=None):
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"""
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:param gramfile: name of file where grammar can be loaded
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:type gramfile: str
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"""
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self._gramfile = (gramfile if gramfile else 'grammars/book_grammars/discourse.fcfg')
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self._parser = load_parser(self._gramfile)
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def parse_to_readings(self, sentence):
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""":see: ReadingCommand.parse_to_readings()"""
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from nltk.sem import root_semrep
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tokens = sentence.split()
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trees = self._parser.parse(tokens)
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return [root_semrep(tree) for tree in trees]
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def combine_readings(self, readings):
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""":see: ReadingCommand.combine_readings()"""
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return reduce(and_, readings)
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def to_fol(self, expression):
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""":see: ReadingCommand.to_fol()"""
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return expression
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class DrtGlueReadingCommand(ReadingCommand):
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def __init__(self, semtype_file=None, remove_duplicates=False,
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depparser=None):
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"""
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:param semtype_file: name of file where grammar can be loaded
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:param remove_duplicates: should duplicates be removed?
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:param depparser: the dependency parser
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"""
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if semtype_file is None:
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semtype_file = os.path.join('grammars', 'sample_grammars','drt_glue.semtype')
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self._glue = DrtGlue(semtype_file=semtype_file,
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remove_duplicates=remove_duplicates,
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depparser=depparser)
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def parse_to_readings(self, sentence):
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""":see: ReadingCommand.parse_to_readings()"""
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return self._glue.parse_to_meaning(sentence)
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def process_thread(self, sentence_readings):
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""":see: ReadingCommand.process_thread()"""
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try:
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return [self.combine_readings(sentence_readings)]
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except AnaphoraResolutionException:
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return []
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def combine_readings(self, readings):
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""":see: ReadingCommand.combine_readings()"""
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thread_reading = reduce(add, readings)
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return resolve_anaphora(thread_reading.simplify())
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def to_fol(self, expression):
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""":see: ReadingCommand.to_fol()"""
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return expression.fol()
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class DiscourseTester(object):
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"""
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Check properties of an ongoing discourse.
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"""
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def __init__(self, input, reading_command=None, background=None):
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"""
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Initialize a ``DiscourseTester``.
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:param input: the discourse sentences
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:type input: list of str
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:param background: Formulas which express background assumptions
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:type background: list(Expression)
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"""
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self._input = input
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self._sentences = dict([('s%s' % i, sent) for i, sent in enumerate(input)])
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self._models = None
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self._readings = {}
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self._reading_command = (reading_command if reading_command else CfgReadingCommand())
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self._threads = {}
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self._filtered_threads = {}
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if background is not None:
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from nltk.sem.logic import Expression
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for e in background:
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assert isinstance(e, Expression)
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self._background = background
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else:
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self._background = []
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###############################
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# Sentences
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###############################
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def sentences(self):
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"""
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Display the list of sentences in the current discourse.
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"""
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for id in sorted(self._sentences):
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print("%s: %s" % (id, self._sentences[id]))
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def add_sentence(self, sentence, informchk=False, consistchk=False,):
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"""
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Add a sentence to the current discourse.
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Updates ``self._input`` and ``self._sentences``.
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:param sentence: An input sentence
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:type sentence: str
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:param informchk: if ``True``, check that the result of adding the sentence is thread-informative. Updates ``self._readings``.
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:param consistchk: if ``True``, check that the result of adding the sentence is thread-consistent. Updates ``self._readings``.
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"""
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# check whether the new sentence is informative (i.e. not entailed by the previous discourse)
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if informchk:
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self.readings(verbose=False)
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for tid in sorted(self._threads):
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assumptions = [reading for (rid, reading) in self.expand_threads(tid)]
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assumptions += self._background
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for sent_reading in self._get_readings(sentence):
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tp = Prover9Command(goal=sent_reading, assumptions=assumptions)
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if tp.prove():
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print("Sentence '%s' under reading '%s':" % (sentence, str(sent_reading)))
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print("Not informative relative to thread '%s'" % tid)
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self._input.append(sentence)
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self._sentences = dict([('s%s' % i, sent) for i, sent in enumerate(self._input)])
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# check whether adding the new sentence to the discourse preserves consistency (i.e. a model can be found for the combined set of
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# of assumptions
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if consistchk:
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self.readings(verbose=False)
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self.models(show=False)
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def retract_sentence(self, sentence, verbose=True):
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"""
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Remove a sentence from the current discourse.
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Updates ``self._input``, ``self._sentences`` and ``self._readings``.
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:param sentence: An input sentence
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:type sentence: str
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:param verbose: If ``True``, report on the updated list of sentences.
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"""
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try:
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self._input.remove(sentence)
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except ValueError:
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print("Retraction failed. The sentence '%s' is not part of the current discourse:" % sentence)
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self.sentences()
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return None
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self._sentences = dict([('s%s' % i, sent) for i, sent in enumerate(self._input)])
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self.readings(verbose=False)
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if verbose:
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print("Current sentences are ")
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self.sentences()
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def grammar(self):
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"""
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Print out the grammar in use for parsing input sentences
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"""
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show_cfg(self._reading_command._gramfile)
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###############################
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# Readings and Threads
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###############################
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def _get_readings(self, sentence):
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"""
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Build a list of semantic readings for a sentence.
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:rtype: list(Expression)
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"""
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return self._reading_command.parse_to_readings(sentence)
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def _construct_readings(self):
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"""
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Use ``self._sentences`` to construct a value for ``self._readings``.
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"""
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# re-initialize self._readings in case we have retracted a sentence
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self._readings = {}
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for sid in sorted(self._sentences):
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sentence = self._sentences[sid]
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readings = self._get_readings(sentence)
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self._readings[sid] = dict([("%s-r%s" % (sid, rid), reading.simplify())
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for rid, reading in enumerate(sorted(readings, key=str))])
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def _construct_threads(self):
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"""
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Use ``self._readings`` to construct a value for ``self._threads``
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and use the model builder to construct a value for ``self._filtered_threads``
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"""
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thread_list = [[]]
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for sid in sorted(self._readings):
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thread_list = self.multiply(thread_list, sorted(self._readings[sid]))
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self._threads = dict([("d%s" % tid, thread) for tid, thread in enumerate(thread_list)])
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# re-initialize the filtered threads
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self._filtered_threads = {}
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# keep the same ids, but only include threads which get models
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consistency_checked = self._check_consistency(self._threads)
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for (tid, thread) in self._threads.items():
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if (tid, True) in consistency_checked:
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self._filtered_threads[tid] = thread
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def _show_readings(self, sentence=None):
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"""
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Print out the readings for the discourse (or a single sentence).
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"""
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if sentence is not None:
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print("The sentence '%s' has these readings:" % sentence)
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for r in [str(reading) for reading in (self._get_readings(sentence))]:
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print(" %s" % r)
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else:
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for sid in sorted(self._readings):
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print()
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print('%s readings:' % sid)
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print() #'-' * 30
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for rid in sorted(self._readings[sid]):
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lf = self._readings[sid][rid]
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print("%s: %s" % (rid, lf.normalize()))
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def _show_threads(self, filter=False, show_thread_readings=False):
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"""
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Print out the value of ``self._threads`` or ``self._filtered_hreads``
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"""
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threads = (self._filtered_threads if filter else self._threads)
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for tid in sorted(threads):
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if show_thread_readings:
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readings = [self._readings[rid.split('-')[0]][rid]
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for rid in self._threads[tid]]
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try:
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thread_reading = ": %s" % \
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self._reading_command.combine_readings(readings).normalize()
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except Exception as e:
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thread_reading = ': INVALID: %s' % e.__class__.__name__
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else:
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thread_reading = ''
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print("%s:" % tid, self._threads[tid], thread_reading)
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def readings(self, sentence=None, threaded=False, verbose=True,
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filter=False, show_thread_readings=False):
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"""
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Construct and show the readings of the discourse (or of a single sentence).
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:param sentence: test just this sentence
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:type sentence: str
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:param threaded: if ``True``, print out each thread ID and the corresponding thread.
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:param filter: if ``True``, only print out consistent thread IDs and threads.
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"""
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self._construct_readings()
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self._construct_threads()
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# if we are filtering or showing thread readings, show threads
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if filter or show_thread_readings:
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threaded = True
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if verbose:
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if not threaded:
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self._show_readings(sentence=sentence)
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else:
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self._show_threads(filter=filter,
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show_thread_readings=show_thread_readings)
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def expand_threads(self, thread_id, threads=None):
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"""
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Given a thread ID, find the list of ``logic.Expression`` objects corresponding to the reading IDs in that thread.
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|
:param thread_id: thread ID
|
||
|
|
:type thread_id: str
|
||
|
|
:param threads: a mapping from thread IDs to lists of reading IDs
|
||
|
|
:type threads: dict
|
||
|
|
:return: A list of pairs ``(rid, reading)`` where reading is the ``logic.Expression`` associated with a reading ID
|
||
|
|
:rtype: list of tuple
|
||
|
|
"""
|
||
|
|
if threads is None:
|
||
|
|
threads = self._threads
|
||
|
|
return [(rid, self._readings[sid][rid]) for rid in threads[thread_id] for sid in rid.split('-')[:1]]
|
||
|
|
|
||
|
|
|
||
|
|
###############################
|
||
|
|
# Models and Background
|
||
|
|
###############################
|
||
|
|
|
||
|
|
def _check_consistency(self, threads, show=False, verbose=False):
|
||
|
|
results = []
|
||
|
|
for tid in sorted(threads):
|
||
|
|
assumptions = [reading for (rid, reading) in self.expand_threads(tid, threads=threads)]
|
||
|
|
assumptions = list(map(self._reading_command.to_fol, self._reading_command.process_thread(assumptions)))
|
||
|
|
if assumptions:
|
||
|
|
assumptions += self._background
|
||
|
|
# if Mace4 finds a model, it always seems to find it quickly
|
||
|
|
mb = MaceCommand(None, assumptions, max_models=20)
|
||
|
|
modelfound = mb.build_model()
|
||
|
|
else:
|
||
|
|
modelfound = False
|
||
|
|
results.append((tid, modelfound))
|
||
|
|
if show:
|
||
|
|
spacer(80)
|
||
|
|
print("Model for Discourse Thread %s" % tid)
|
||
|
|
spacer(80)
|
||
|
|
if verbose:
|
||
|
|
for a in assumptions:
|
||
|
|
print(a)
|
||
|
|
spacer(80)
|
||
|
|
if modelfound:
|
||
|
|
print(mb.model(format='cooked'))
|
||
|
|
else:
|
||
|
|
print("No model found!\n")
|
||
|
|
return results
|
||
|
|
|
||
|
|
def models(self, thread_id=None, show=True, verbose=False):
|
||
|
|
"""
|
||
|
|
Call Mace4 to build a model for each current discourse thread.
|
||
|
|
|
||
|
|
:param thread_id: thread ID
|
||
|
|
:type thread_id: str
|
||
|
|
:param show: If ``True``, display the model that has been found.
|
||
|
|
"""
|
||
|
|
self._construct_readings()
|
||
|
|
self._construct_threads()
|
||
|
|
threads = ({thread_id: self._threads[thread_id]} if thread_id else self._threads)
|
||
|
|
|
||
|
|
for (tid, modelfound) in self._check_consistency(threads, show=show, verbose=verbose):
|
||
|
|
idlist = [rid for rid in threads[tid]]
|
||
|
|
|
||
|
|
if not modelfound:
|
||
|
|
print("Inconsistent discourse: %s %s:" % (tid, idlist))
|
||
|
|
for rid, reading in self.expand_threads(tid):
|
||
|
|
print(" %s: %s" % (rid, reading.normalize()))
|
||
|
|
print()
|
||
|
|
else:
|
||
|
|
print("Consistent discourse: %s %s:" % (tid, idlist))
|
||
|
|
for rid, reading in self.expand_threads(tid):
|
||
|
|
print(" %s: %s" % (rid, reading.normalize()))
|
||
|
|
print()
|
||
|
|
|
||
|
|
def add_background(self, background, verbose=False):
|
||
|
|
"""
|
||
|
|
Add a list of background assumptions for reasoning about the discourse.
|
||
|
|
|
||
|
|
When called, this method also updates the discourse model's set of readings and threads.
|
||
|
|
:param background: Formulas which contain background information
|
||
|
|
:type background: list(Expression)
|
||
|
|
"""
|
||
|
|
from nltk.sem.logic import Expression
|
||
|
|
for (count, e) in enumerate(background):
|
||
|
|
assert isinstance(e, Expression)
|
||
|
|
if verbose:
|
||
|
|
print("Adding assumption %s to background" % count)
|
||
|
|
self._background.append(e)
|
||
|
|
|
||
|
|
#update the state
|
||
|
|
self._construct_readings()
|
||
|
|
self._construct_threads()
|
||
|
|
|
||
|
|
def background(self):
|
||
|
|
"""
|
||
|
|
Show the current background assumptions.
|
||
|
|
"""
|
||
|
|
for e in self._background:
|
||
|
|
print(str(e))
|
||
|
|
|
||
|
|
###############################
|
||
|
|
# Misc
|
||
|
|
###############################
|
||
|
|
|
||
|
|
@staticmethod
|
||
|
|
def multiply(discourse, readings):
|
||
|
|
"""
|
||
|
|
Multiply every thread in ``discourse`` by every reading in ``readings``.
|
||
|
|
|
||
|
|
Given discourse = [['A'], ['B']], readings = ['a', 'b', 'c'] , returns
|
||
|
|
[['A', 'a'], ['A', 'b'], ['A', 'c'], ['B', 'a'], ['B', 'b'], ['B', 'c']]
|
||
|
|
|
||
|
|
:param discourse: the current list of readings
|
||
|
|
:type discourse: list of lists
|
||
|
|
:param readings: an additional list of readings
|
||
|
|
:type readings: list(Expression)
|
||
|
|
:rtype: A list of lists
|
||
|
|
"""
|
||
|
|
result = []
|
||
|
|
for sublist in discourse:
|
||
|
|
for r in readings:
|
||
|
|
new = []
|
||
|
|
new += sublist
|
||
|
|
new.append(r)
|
||
|
|
result.append(new)
|
||
|
|
return result
|
||
|
|
|
||
|
|
#multiply = DiscourseTester.multiply
|
||
|
|
#L1 = [['A'], ['B']]
|
||
|
|
#L2 = ['a', 'b', 'c']
|
||
|
|
#print multiply(L1,L2)
|
||
|
|
|
||
|
|
|
||
|
|
def load_fol(s):
|
||
|
|
"""
|
||
|
|
Temporarily duplicated from ``nltk.sem.util``.
|
||
|
|
Convert a file of first order formulas into a list of ``Expression`` objects.
|
||
|
|
|
||
|
|
:param s: the contents of the file
|
||
|
|
:type s: str
|
||
|
|
:return: a list of parsed formulas.
|
||
|
|
:rtype: list(Expression)
|
||
|
|
"""
|
||
|
|
statements = []
|
||
|
|
for linenum, line in enumerate(s.splitlines()):
|
||
|
|
line = line.strip()
|
||
|
|
if line.startswith('#') or line == '':
|
||
|
|
continue
|
||
|
|
try:
|
||
|
|
statements.append(Expression.fromstring(line))
|
||
|
|
except Exception:
|
||
|
|
raise ValueError('Unable to parse line %s: %s' % (linenum, line))
|
||
|
|
return statements
|
||
|
|
|
||
|
|
|
||
|
|
###############################
|
||
|
|
# Demo
|
||
|
|
###############################
|
||
|
|
def discourse_demo(reading_command=None):
|
||
|
|
"""
|
||
|
|
Illustrate the various methods of ``DiscourseTester``
|
||
|
|
"""
|
||
|
|
dt = DiscourseTester(['A boxer walks', 'Every boxer chases a girl'],
|
||
|
|
reading_command)
|
||
|
|
dt.models()
|
||
|
|
print()
|
||
|
|
# dt.grammar()
|
||
|
|
print()
|
||
|
|
dt.sentences()
|
||
|
|
print()
|
||
|
|
dt.readings()
|
||
|
|
print()
|
||
|
|
dt.readings(threaded=True)
|
||
|
|
print()
|
||
|
|
dt.models('d1')
|
||
|
|
dt.add_sentence('John is a boxer')
|
||
|
|
print()
|
||
|
|
dt.sentences()
|
||
|
|
print()
|
||
|
|
dt.readings(threaded=True)
|
||
|
|
print()
|
||
|
|
dt = DiscourseTester(['A student dances', 'Every student is a person'],
|
||
|
|
reading_command)
|
||
|
|
print()
|
||
|
|
dt.add_sentence('No person dances', consistchk=True)
|
||
|
|
print()
|
||
|
|
dt.readings()
|
||
|
|
print()
|
||
|
|
dt.retract_sentence('No person dances', verbose=True)
|
||
|
|
print()
|
||
|
|
dt.models()
|
||
|
|
print()
|
||
|
|
dt.readings('A person dances')
|
||
|
|
print()
|
||
|
|
dt.add_sentence('A person dances', informchk=True)
|
||
|
|
dt = DiscourseTester(['Vincent is a boxer', 'Fido is a boxer',
|
||
|
|
'Vincent is married', 'Fido barks'],
|
||
|
|
reading_command)
|
||
|
|
dt.readings(filter=True)
|
||
|
|
import nltk.data
|
||
|
|
background_file = os.path.join('grammars', 'book_grammars', 'background.fol')
|
||
|
|
background = nltk.data.load(background_file)
|
||
|
|
|
||
|
|
print()
|
||
|
|
dt.add_background(background, verbose=False)
|
||
|
|
dt.background()
|
||
|
|
print()
|
||
|
|
dt.readings(filter=True)
|
||
|
|
print()
|
||
|
|
dt.models()
|
||
|
|
|
||
|
|
|
||
|
|
def drt_discourse_demo(reading_command=None):
|
||
|
|
"""
|
||
|
|
Illustrate the various methods of ``DiscourseTester``
|
||
|
|
"""
|
||
|
|
dt = DiscourseTester(['every dog chases a boy', 'he runs'],
|
||
|
|
reading_command)
|
||
|
|
dt.models()
|
||
|
|
print()
|
||
|
|
dt.sentences()
|
||
|
|
print()
|
||
|
|
dt.readings()
|
||
|
|
print()
|
||
|
|
dt.readings(show_thread_readings=True)
|
||
|
|
print()
|
||
|
|
dt.readings(filter=True, show_thread_readings=True)
|
||
|
|
|
||
|
|
|
||
|
|
def spacer(num=30):
|
||
|
|
print('-' * num)
|
||
|
|
|
||
|
|
|
||
|
|
def demo():
|
||
|
|
discourse_demo()
|
||
|
|
|
||
|
|
tagger = RegexpTagger([('^(chases|runs)$', 'VB'),
|
||
|
|
('^(a)$', 'ex_quant'),
|
||
|
|
('^(every)$', 'univ_quant'),
|
||
|
|
('^(dog|boy)$', 'NN'),
|
||
|
|
('^(he)$', 'PRP')])
|
||
|
|
depparser = MaltParser(tagger=tagger)
|
||
|
|
drt_discourse_demo(DrtGlueReadingCommand(remove_duplicates=False,
|
||
|
|
depparser=depparser))
|
||
|
|
|
||
|
|
|
||
|
|
if __name__ == '__main__':
|
||
|
|
demo()
|