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236 lines
8.3 KiB
Python
236 lines
8.3 KiB
Python
# Natural Language Toolkit: Parser Utility Functions
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#
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# Author: Ewan Klein <ewan@inf.ed.ac.uk>
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#
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# Copyright (C) 2001-2020 NLTK Project
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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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Utility functions for parsers.
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"""
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from nltk.grammar import CFG, FeatureGrammar, PCFG
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from nltk.data import load
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from nltk.parse.chart import Chart, ChartParser
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from nltk.parse.pchart import InsideChartParser
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from nltk.parse.featurechart import FeatureChart, FeatureChartParser
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def load_parser(
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grammar_url, trace=0, parser=None, chart_class=None, beam_size=0, **load_args
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):
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"""
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Load a grammar from a file, and build a parser based on that grammar.
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The parser depends on the grammar format, and might also depend
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on properties of the grammar itself.
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The following grammar formats are currently supported:
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- ``'cfg'`` (CFGs: ``CFG``)
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- ``'pcfg'`` (probabilistic CFGs: ``PCFG``)
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- ``'fcfg'`` (feature-based CFGs: ``FeatureGrammar``)
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:type grammar_url: str
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:param grammar_url: A URL specifying where the grammar is located.
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The default protocol is ``"nltk:"``, which searches for the file
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in the the NLTK data package.
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:type trace: int
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:param trace: The level of tracing that should be used when
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parsing a text. ``0`` will generate no tracing output;
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and higher numbers will produce more verbose tracing output.
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:param parser: The class used for parsing; should be ``ChartParser``
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or a subclass.
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If None, the class depends on the grammar format.
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:param chart_class: The class used for storing the chart;
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should be ``Chart`` or a subclass.
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Only used for CFGs and feature CFGs.
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If None, the chart class depends on the grammar format.
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:type beam_size: int
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:param beam_size: The maximum length for the parser's edge queue.
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Only used for probabilistic CFGs.
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:param load_args: Keyword parameters used when loading the grammar.
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See ``data.load`` for more information.
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"""
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grammar = load(grammar_url, **load_args)
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if not isinstance(grammar, CFG):
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raise ValueError("The grammar must be a CFG, " "or a subclass thereof.")
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if isinstance(grammar, PCFG):
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if parser is None:
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parser = InsideChartParser
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return parser(grammar, trace=trace, beam_size=beam_size)
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elif isinstance(grammar, FeatureGrammar):
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if parser is None:
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parser = FeatureChartParser
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if chart_class is None:
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chart_class = FeatureChart
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return parser(grammar, trace=trace, chart_class=chart_class)
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else: # Plain CFG.
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if parser is None:
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parser = ChartParser
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if chart_class is None:
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chart_class = Chart
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return parser(grammar, trace=trace, chart_class=chart_class)
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def taggedsent_to_conll(sentence):
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"""
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A module to convert a single POS tagged sentence into CONLL format.
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>>> from nltk import word_tokenize, pos_tag
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>>> text = "This is a foobar sentence."
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>>> for line in taggedsent_to_conll(pos_tag(word_tokenize(text))):
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... print(line, end="")
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1 This _ DT DT _ 0 a _ _
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2 is _ VBZ VBZ _ 0 a _ _
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3 a _ DT DT _ 0 a _ _
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4 foobar _ JJ JJ _ 0 a _ _
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5 sentence _ NN NN _ 0 a _ _
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6 . _ . . _ 0 a _ _
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:param sentence: A single input sentence to parse
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:type sentence: list(tuple(str, str))
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:rtype: iter(str)
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:return: a generator yielding a single sentence in CONLL format.
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"""
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for (i, (word, tag)) in enumerate(sentence, start=1):
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input_str = [str(i), word, "_", tag, tag, "_", "0", "a", "_", "_"]
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input_str = "\t".join(input_str) + "\n"
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yield input_str
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def taggedsents_to_conll(sentences):
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"""
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A module to convert the a POS tagged document stream
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(i.e. list of list of tuples, a list of sentences) and yield lines
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in CONLL format. This module yields one line per word and two newlines
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for end of sentence.
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>>> from nltk import word_tokenize, sent_tokenize, pos_tag
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>>> text = "This is a foobar sentence. Is that right?"
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>>> sentences = [pos_tag(word_tokenize(sent)) for sent in sent_tokenize(text)]
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>>> for line in taggedsents_to_conll(sentences):
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... if line:
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... print(line, end="")
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1 This _ DT DT _ 0 a _ _
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2 is _ VBZ VBZ _ 0 a _ _
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3 a _ DT DT _ 0 a _ _
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4 foobar _ JJ JJ _ 0 a _ _
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5 sentence _ NN NN _ 0 a _ _
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6 . _ . . _ 0 a _ _
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<BLANKLINE>
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<BLANKLINE>
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1 Is _ VBZ VBZ _ 0 a _ _
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2 that _ IN IN _ 0 a _ _
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3 right _ NN NN _ 0 a _ _
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4 ? _ . . _ 0 a _ _
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<BLANKLINE>
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<BLANKLINE>
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:param sentences: Input sentences to parse
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:type sentence: list(list(tuple(str, str)))
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:rtype: iter(str)
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:return: a generator yielding sentences in CONLL format.
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"""
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for sentence in sentences:
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for input_str in taggedsent_to_conll(sentence):
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yield input_str
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yield "\n\n"
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######################################################################
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# { Test Suites
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######################################################################
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class TestGrammar(object):
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"""
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Unit tests for CFG.
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"""
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def __init__(self, grammar, suite, accept=None, reject=None):
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self.test_grammar = grammar
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self.cp = load_parser(grammar, trace=0)
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self.suite = suite
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self._accept = accept
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self._reject = reject
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def run(self, show_trees=False):
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"""
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Sentences in the test suite are divided into two classes:
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- grammatical (``accept``) and
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- ungrammatical (``reject``).
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If a sentence should parse accordng to the grammar, the value of
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``trees`` will be a non-empty list. If a sentence should be rejected
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according to the grammar, then the value of ``trees`` will be None.
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"""
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for test in self.suite:
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print(test["doc"] + ":", end=" ")
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for key in ["accept", "reject"]:
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for sent in test[key]:
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tokens = sent.split()
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trees = list(self.cp.parse(tokens))
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if show_trees and trees:
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print()
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print(sent)
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for tree in trees:
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print(tree)
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if key == "accept":
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if trees == []:
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raise ValueError("Sentence '%s' failed to parse'" % sent)
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else:
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accepted = True
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else:
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if trees:
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raise ValueError("Sentence '%s' received a parse'" % sent)
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else:
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rejected = True
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if accepted and rejected:
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print("All tests passed!")
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def extract_test_sentences(string, comment_chars="#%;", encoding=None):
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"""
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Parses a string with one test sentence per line.
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Lines can optionally begin with:
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- a bool, saying if the sentence is grammatical or not, or
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- an int, giving the number of parse trees is should have,
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The result information is followed by a colon, and then the sentence.
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Empty lines and lines beginning with a comment char are ignored.
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:return: a list of tuple of sentences and expected results,
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where a sentence is a list of str,
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and a result is None, or bool, or int
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:param comment_chars: ``str`` of possible comment characters.
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:param encoding: the encoding of the string, if it is binary
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"""
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if encoding is not None:
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string = string.decode(encoding)
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sentences = []
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for sentence in string.split("\n"):
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if sentence == "" or sentence[0] in comment_chars:
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continue
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split_info = sentence.split(":", 1)
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result = None
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if len(split_info) == 2:
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if split_info[0] in ["True", "true", "False", "false"]:
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result = split_info[0] in ["True", "true"]
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sentence = split_info[1]
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else:
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result = int(split_info[0])
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sentence = split_info[1]
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tokens = sentence.split()
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if tokens == []:
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continue
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sentences += [(tokens, result)]
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return sentences
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# nose thinks it is a test
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extract_test_sentences.__test__ = False
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