laywerrobot/lib/python3.6/site-packages/nltk/parse/bllip.py
2020-08-27 21:55:39 +02:00

282 lines
11 KiB
Python

# Natural Language Toolkit: Interface to BLLIP Parser
#
# Author: David McClosky <dmcc@bigasterisk.com>
#
# Copyright (C) 2001-2018 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))
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)
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)
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)
@classmethod
def from_unified_model_dir(this_class, 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 this_class(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')