Source code for nltk.parse.bllip

# Natural Language Toolkit: Interface to BLLIP Parser
#
# Author: David McClosky <dmcc@bigasterisk.com>
#
# Copyright (C) 2001-2019 NLTK Project
# URL: <http://nltk.org/>
# For license information, see LICENSE.TXT

from __future__ import print_function

from nltk.parse.api import ParserI
from nltk.tree import Tree

"""
Interface for parsing with BLLIP Parser. Requires the Python
bllipparser module. BllipParser objects can be constructed with the
``BllipParser.from_unified_model_dir`` class method or manually using the
``BllipParser`` constructor. The former is generally easier if you have
a BLLIP Parser unified model directory -- a basic model can be obtained
from NLTK's downloader. More unified parsing models can be obtained with
BLLIP Parser's ModelFetcher (run ``python -m bllipparser.ModelFetcher``
or see docs for ``bllipparser.ModelFetcher.download_and_install_model``).

Basic usage::

    # download and install a basic unified parsing model (Wall Street Journal)
    # sudo python -m nltk.downloader bllip_wsj_no_aux

    >>> from nltk.data import find
    >>> model_dir = find('models/bllip_wsj_no_aux').path
    >>> bllip = BllipParser.from_unified_model_dir(model_dir)

    # 1-best parsing
    >>> sentence1 = 'British left waffles on Falklands .'.split()
    >>> top_parse = bllip.parse_one(sentence1)
    >>> print(top_parse)
    (S1
      (S
        (NP (JJ British) (NN left))
        (VP (VBZ waffles) (PP (IN on) (NP (NNP Falklands))))
        (. .)))

    # n-best parsing
    >>> sentence2 = 'Time flies'.split()
    >>> all_parses = bllip.parse_all(sentence2)
    >>> print(len(all_parses))
    50
    >>> print(all_parses[0])
    (S1 (S (NP (NNP Time)) (VP (VBZ flies))))

    # incorporating external tagging constraints (None means unconstrained tag)
    >>> constrained1 = bllip.tagged_parse([('Time', 'VB'), ('flies', 'NNS')])
    >>> print(next(constrained1))
    (S1 (NP (VB Time) (NNS flies)))
    >>> constrained2 = bllip.tagged_parse([('Time', 'NN'), ('flies', None)])
    >>> print(next(constrained2))
    (S1 (NP (NN Time) (VBZ flies)))

References
----------

- Charniak, Eugene. "A maximum-entropy-inspired parser." Proceedings of
  the 1st North American chapter of the Association for Computational
  Linguistics conference. Association for Computational Linguistics,
  2000.

- Charniak, Eugene, and Mark Johnson. "Coarse-to-fine n-best parsing
  and MaxEnt discriminative reranking." Proceedings of the 43rd Annual
  Meeting on Association for Computational Linguistics. Association
  for Computational Linguistics, 2005.

Known issues
------------

Note that BLLIP Parser is not currently threadsafe. Since this module
uses a SWIG interface, it is potentially unsafe to create multiple
``BllipParser`` objects in the same process. BLLIP Parser currently
has issues with non-ASCII text and will raise an error if given any.

See http://pypi.python.org/pypi/bllipparser/ for more information
on BLLIP Parser's Python interface.
"""

__all__ = ['BllipParser']

# this block allows this module to be imported even if bllipparser isn't
# available
try:
    from bllipparser import RerankingParser
    from bllipparser.RerankingParser import get_unified_model_parameters

    def _ensure_bllip_import_or_error():
        pass


except ImportError as ie:

    def _ensure_bllip_import_or_error(ie=ie):
        raise ImportError("Couldn't import bllipparser module: %s" % ie)


def _ensure_ascii(words):
    try:
        for i, word in enumerate(words):
            word.decode('ascii')
    except UnicodeDecodeError:
        raise ValueError(
            "Token %d (%r) is non-ASCII. BLLIP Parser "
            "currently doesn't support non-ASCII inputs." % (i, word)
        )


def _scored_parse_to_nltk_tree(scored_parse):
    return Tree.fromstring(str(scored_parse.ptb_parse))


[docs]class BllipParser(ParserI): """ Interface for parsing with BLLIP Parser. BllipParser objects can be constructed with the ``BllipParser.from_unified_model_dir`` class method or manually using the ``BllipParser`` constructor. """ def __init__( self, parser_model=None, reranker_features=None, reranker_weights=None, parser_options=None, reranker_options=None, ): """ Load a BLLIP Parser model from scratch. You'll typically want to use the ``from_unified_model_dir()`` class method to construct this object. :param parser_model: Path to parser model directory :type parser_model: str :param reranker_features: Path the reranker model's features file :type reranker_features: str :param reranker_weights: Path the reranker model's weights file :type reranker_weights: str :param parser_options: optional dictionary of parser options, see ``bllipparser.RerankingParser.RerankingParser.load_parser_options()`` for more information. :type parser_options: dict(str) :param reranker_options: optional dictionary of reranker options, see ``bllipparser.RerankingParser.RerankingParser.load_reranker_model()`` for more information. :type reranker_options: dict(str) """ _ensure_bllip_import_or_error() parser_options = parser_options or {} reranker_options = reranker_options or {} self.rrp = RerankingParser() self.rrp.load_parser_model(parser_model, **parser_options) if reranker_features and reranker_weights: self.rrp.load_reranker_model( features_filename=reranker_features, weights_filename=reranker_weights, **reranker_options )
[docs] def parse(self, sentence): """ Use BLLIP Parser to parse a sentence. Takes a sentence as a list of words; it will be automatically tagged with this BLLIP Parser instance's tagger. :return: An iterator that generates parse trees for the sentence from most likely to least likely. :param sentence: The sentence to be parsed :type sentence: list(str) :rtype: iter(Tree) """ _ensure_ascii(sentence) nbest_list = self.rrp.parse(sentence) for scored_parse in nbest_list: yield _scored_parse_to_nltk_tree(scored_parse)
[docs] def tagged_parse(self, word_and_tag_pairs): """ Use BLLIP to parse a sentence. Takes a sentence as a list of (word, tag) tuples; the sentence must have already been tokenized and tagged. BLLIP will attempt to use the tags provided but may use others if it can't come up with a complete parse subject to those constraints. You may also specify a tag as ``None`` to leave a token's tag unconstrained. :return: An iterator that generates parse trees for the sentence from most likely to least likely. :param sentence: Input sentence to parse as (word, tag) pairs :type sentence: list(tuple(str, str)) :rtype: iter(Tree) """ words = [] tag_map = {} for i, (word, tag) in enumerate(word_and_tag_pairs): words.append(word) if tag is not None: tag_map[i] = tag _ensure_ascii(words) nbest_list = self.rrp.parse_tagged(words, tag_map) for scored_parse in nbest_list: yield _scored_parse_to_nltk_tree(scored_parse)
[docs] @classmethod def from_unified_model_dir( cls, model_dir, parser_options=None, reranker_options=None ): """ Create a ``BllipParser`` object from a unified parsing model directory. Unified parsing model directories are a standardized way of storing BLLIP parser and reranker models together on disk. See ``bllipparser.RerankingParser.get_unified_model_parameters()`` for more information about unified model directories. :return: A ``BllipParser`` object using the parser and reranker models in the model directory. :param model_dir: Path to the unified model directory. :type model_dir: str :param parser_options: optional dictionary of parser options, see ``bllipparser.RerankingParser.RerankingParser.load_parser_options()`` for more information. :type parser_options: dict(str) :param reranker_options: optional dictionary of reranker options, see ``bllipparser.RerankingParser.RerankingParser.load_reranker_model()`` for more information. :type reranker_options: dict(str) :rtype: BllipParser """ ( parser_model_dir, reranker_features_filename, reranker_weights_filename, ) = get_unified_model_parameters(model_dir) return cls( parser_model_dir, reranker_features_filename, reranker_weights_filename, parser_options, reranker_options, )
def demo(): """This assumes the Python module bllipparser is installed.""" # download and install a basic unified parsing model (Wall Street Journal) # sudo python -m nltk.downloader bllip_wsj_no_aux from nltk.data import find model_dir = find('models/bllip_wsj_no_aux').path print('Loading BLLIP Parsing models...') # the easiest way to get started is to use a unified model bllip = BllipParser.from_unified_model_dir(model_dir) print('Done.') sentence1 = 'British left waffles on Falklands .'.split() sentence2 = 'I saw the man with the telescope .'.split() # this sentence is known to fail under the WSJ parsing model fail1 = '# ! ? : -'.split() for sentence in (sentence1, sentence2, fail1): print('Sentence: %r' % ' '.join(sentence)) try: tree = next(bllip.parse(sentence)) print(tree) except StopIteration: print("(parse failed)") # n-best parsing demo for i, parse in enumerate(bllip.parse(sentence1)): print('parse %d:\n%s' % (i, parse)) # using external POS tag constraints print( "forcing 'tree' to be 'NN':", next(bllip.tagged_parse([('A', None), ('tree', 'NN')])), ) print( "forcing 'A' to be 'DT' and 'tree' to be 'NNP':", next(bllip.tagged_parse([('A', 'DT'), ('tree', 'NNP')])), ) # constraints don't have to make sense... (though on more complicated # sentences, they may cause the parse to fail) print( "forcing 'A' to be 'NNP':", next(bllip.tagged_parse([('A', 'NNP'), ('tree', None)])), ) def setup_module(module): from nose import SkipTest try: _ensure_bllip_import_or_error() except ImportError: raise SkipTest( 'doctests from nltk.parse.bllip are skipped because ' 'the bllipparser module is not installed' )