/nltk/sem/util.py
Python | 305 lines | 237 code | 13 blank | 55 comment | 13 complexity | b6197b9e6fb488f5e57bb866ca75e4ca MD5 | raw file
Possible License(s): Apache-2.0
- # Natural Language Toolkit: Semantic Interpretation
- #
- # Author: Ewan Klein <ewan@inf.ed.ac.uk>
- #
- # Copyright (C) 2001-2012 NLTK Project
- # URL: <http://www.nltk.org/>
- # For license information, see LICENSE.TXT
- """
- Utility functions for batch-processing sentences: parsing and
- extraction of the semantic representation of the root node of the the
- syntax tree, followed by evaluation of the semantic representation in
- a first-order model.
- """
- import evaluate
- import re
- ##############################################################
- ## Utility functions for connecting parse output to semantics
- ##############################################################
- def batch_parse(inputs, grammar, trace=0):
- """
- Convert input sentences into syntactic trees.
- :param inputs: sentences to be parsed
- :type inputs: list of str
- :param grammar: ``FeatureGrammar`` or name of feature-based grammar
- :rtype: dict
- :return: a mapping from input sentences to a list of ``Tree``s
- """
- # put imports here to avoid circult dependencies
- from nltk.grammar import FeatureGrammar
- from nltk.parse import FeatureChartParser, load_parser
- if isinstance(grammar, FeatureGrammar):
- cp = FeatureChartParser(grammar)
- else:
- cp = load_parser(grammar, trace=trace)
- parses = []
- for sent in inputs:
- tokens = sent.split() # use a tokenizer?
- syntrees = cp.nbest_parse(tokens)
- parses.append(syntrees)
- return parses
- def root_semrep(syntree, semkey='SEM'):
- """
- Find the semantic representation at the root of a tree.
- :param syntree: a parse ``Tree``
- :param semkey: the feature label to use for the root semantics in the tree
- :return: the semantic representation at the root of a ``Tree``
- :rtype: sem.Expression
- """
- from nltk.grammar import FeatStructNonterminal
- node = syntree.node
- assert isinstance(node, FeatStructNonterminal)
- try:
- return node[semkey]
- except KeyError:
- print node,
- print "has no specification for the feature %s" % semkey
- raise
- def batch_interpret(inputs, grammar, semkey='SEM', trace=0):
- """
- Add the semantic representation to each syntactic parse tree
- of each input sentence.
- :param inputs: a list of sentences
- :param grammar: ``FeatureGrammar`` or name of feature-based grammar
- :return: a mapping from sentences to lists of pairs (parse-tree, semantic-representations)
- :rtype: dict
- """
- return [[(syn, root_semrep(syn, semkey)) for syn in syntrees]
- for syntrees in batch_parse(inputs, grammar, trace=trace)]
- def batch_evaluate(inputs, grammar, model, assignment, trace=0):
- """
- Add the truth-in-a-model value to each semantic representation
- for each syntactic parse of each input sentences.
- :param inputs: a list of sentences
- :param grammar: ``FeatureGrammar`` or name of feature-based grammar
- :return: a mapping from sentences to lists of triples (parse-tree, semantic-representations, evaluation-in-model)
- :rtype: dict
- """
- return [[(syn, sem, model.evaluate(str(sem), assignment, trace=trace))
- for (syn, sem) in interpretations]
- for interpretations in batch_interpret(inputs, grammar)]
- ##########################################
- # REs used by the parse_valuation function
- ##########################################
- _VAL_SPLIT_RE = re.compile(r'\s*=+>\s*')
- _ELEMENT_SPLIT_RE = re.compile(r'\s*,\s*')
- _TUPLES_RE = re.compile(r"""\s*
- (\([^)]+\)) # tuple-expression
- \s*""", re.VERBOSE)
- def parse_valuation_line(s):
- """
- Parse a line in a valuation file.
- Lines are expected to be of the form::
- noosa => n
- girl => {g1, g2}
- chase => {(b1, g1), (b2, g1), (g1, d1), (g2, d2)}
- :param s: input line
- :type s: str
- :return: a pair (symbol, value)
- :rtype: tuple
- """
- pieces = _VAL_SPLIT_RE.split(s)
- symbol = pieces[0]
- value = pieces[1]
- # check whether the value is meant to be a set
- if value.startswith('{'):
- value = value[1:-1]
- tuple_strings = _TUPLES_RE.findall(value)
- # are the set elements tuples?
- if tuple_strings:
- set_elements = []
- for ts in tuple_strings:
- ts = ts[1:-1]
- element = tuple(_ELEMENT_SPLIT_RE.split(ts))
- set_elements.append(element)
- else:
- set_elements = _ELEMENT_SPLIT_RE.split(value)
- value = set(set_elements)
- return symbol, value
- def parse_valuation(s):
- """
- Convert a valuation file into a valuation.
- :param s: the contents of a valuation file
- :type s: str
- :return: a ``nltk.sem`` valuation
- :rtype: Valuation
- """
- statements = []
- for linenum, line in enumerate(s.splitlines()):
- line = line.strip()
- if line.startswith('#') or line=='': continue
- try: statements.append(parse_valuation_line(line))
- except ValueError:
- raise ValueError, 'Unable to parse line %s: %s' % (linenum, line)
- val = evaluate.Valuation(statements)
- return val
- def demo_model0():
- global m0, g0
- #Initialize a valuation of non-logical constants."""
- v = [('john', 'b1'),
- ('mary', 'g1'),
- ('suzie', 'g2'),
- ('fido', 'd1'),
- ('tess', 'd2'),
- ('noosa', 'n'),
- ('girl', set(['g1', 'g2'])),
- ('boy', set(['b1', 'b2'])),
- ('dog', set(['d1', 'd2'])),
- ('bark', set(['d1', 'd2'])),
- ('walk', set(['b1', 'g2', 'd1'])),
- ('chase', set([('b1', 'g1'), ('b2', 'g1'), ('g1', 'd1'), ('g2', 'd2')])),
- ('see', set([('b1', 'g1'), ('b2', 'd2'), ('g1', 'b1'),('d2', 'b1'), ('g2', 'n')])),
- ('in', set([('b1', 'n'), ('b2', 'n'), ('d2', 'n')])),
- ('with', set([('b1', 'g1'), ('g1', 'b1'), ('d1', 'b1'), ('b1', 'd1')]))
- ]
- #Read in the data from ``v``
- val = evaluate.Valuation(v)
- #Bind ``dom`` to the ``domain`` property of ``val``
- dom = val.domain
- #Initialize a model with parameters ``dom`` and ``val``.
- m0 = evaluate.Model(dom, val)
- #Initialize a variable assignment with parameter ``dom``
- g0 = evaluate.Assignment(dom)
- def read_sents(file):
- sents = [l.rstrip() for l in open(file)]
- # get rid of blank lines
- sents = [l for l in sents if len(l) > 0]
- sents = [l for l in sents if not l[0] == '#']
- return sents
- def demo_legacy_grammar():
- """
- Check that batch_interpret() is compatible with legacy grammars that use
- a lowercase 'sem' feature.
- Define 'test.fcfg' to be the following
- """
- from nltk.grammar import parse_fcfg
- g = parse_fcfg("""
- % start S
- S[sem=<hello>] -> 'hello'
- """)
- print "Reading grammar: %s" % g
- print "*" * 20
- for reading in batch_interpret(['hello'], g, semkey='sem'):
- syn, sem = reading[0]
- print
- print "output: ", sem
- def demo():
- import sys
- from optparse import OptionParser
- description = \
- """
- Parse and evaluate some sentences.
- """
- opts = OptionParser(description=description)
- opts.set_defaults(evaluate=True, beta=True, syntrace=0,
- semtrace=0, demo='default', grammar='', sentences='')
- opts.add_option("-d", "--demo", dest="demo",
- help="choose demo D; omit this for the default demo, or specify 'chat80'", metavar="D")
- opts.add_option("-g", "--gram", dest="grammar",
- help="read in grammar G", metavar="G")
- opts.add_option("-m", "--model", dest="model",
- help="import model M (omit '.py' suffix)", metavar="M")
- opts.add_option("-s", "--sentences", dest="sentences",
- help="read in a file of test sentences S", metavar="S")
- opts.add_option("-e", "--no-eval", action="store_false", dest="evaluate",
- help="just do a syntactic analysis")
- opts.add_option("-b", "--no-beta-reduction", action="store_false",
- dest="beta", help="don't carry out beta-reduction")
- opts.add_option("-t", "--syntrace", action="count", dest="syntrace",
- help="set syntactic tracing on; requires '-e' option")
- opts.add_option("-T", "--semtrace", action="count", dest="semtrace",
- help="set semantic tracing on")
- (options, args) = opts.parse_args()
- SPACER = '-' * 30
- demo_model0()
- sents = [
- 'Fido sees a boy with Mary',
- 'John sees Mary',
- 'every girl chases a dog',
- 'every boy chases a girl',
- 'John walks with a girl in Noosa',
- 'who walks']
- gramfile = 'grammars/sample_grammars/sem2.fcfg'
- if options.sentences:
- sentsfile = options.sentences
- if options.grammar:
- gramfile = options.grammar
- if options.model:
- exec "import %s as model" % options.model
- if sents is None:
- sents = read_sents(sentsfile)
- # Set model and assignment
- model = m0
- g = g0
- if options.evaluate:
- evaluations = \
- batch_evaluate(sents, gramfile, model, g, trace=options.semtrace)
- else:
- semreps = \
- batch_interpret(sents, gramfile, trace=options.syntrace)
- for i, sent in enumerate(sents):
- n = 1
- print '\nSentence: %s' % sent
- print SPACER
- if options.evaluate:
- for (syntree, semrep, value) in evaluations[i]:
- if isinstance(value, dict):
- value = set(value.keys())
- print '%d: %s' % (n, semrep)
- print value
- n += 1
- else:
- for (syntree, semrep) in semreps[i]:
- print '%d: %s' % (n, semrep)
- n += 1
- if __name__ == "__main__":
- #demo()
- demo_legacy_grammar()