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(
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,
)
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(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,
)
