394 lines
16 KiB
Python
394 lines
16 KiB
Python
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# Natural Language Toolkit: Interface to MaltParser
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#
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# Author: Dan Garrette <dhgarrette@gmail.com>
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# Contributor: Liling Tan, Mustufain, osamamukhtar11
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#
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# Copyright (C) 2001-2023 NLTK Project
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# URL: <https://www.nltk.org/>
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# For license information, see LICENSE.TXT
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import inspect
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import os
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import subprocess
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import sys
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import tempfile
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from nltk.data import ZipFilePathPointer
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from nltk.internals import find_dir, find_file, find_jars_within_path
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from nltk.parse.api import ParserI
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from nltk.parse.dependencygraph import DependencyGraph
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from nltk.parse.util import taggedsents_to_conll
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def malt_regex_tagger():
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from nltk.tag import RegexpTagger
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_tagger = RegexpTagger(
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[
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(r"\.$", "."),
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(r"\,$", ","),
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(r"\?$", "?"), # fullstop, comma, Qmark
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(r"\($", "("),
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(r"\)$", ")"), # round brackets
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(r"\[$", "["),
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(r"\]$", "]"), # square brackets
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(r"^-?[0-9]+(\.[0-9]+)?$", "CD"), # cardinal numbers
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(r"(The|the|A|a|An|an)$", "DT"), # articles
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(r"(He|he|She|she|It|it|I|me|Me|You|you)$", "PRP"), # pronouns
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(r"(His|his|Her|her|Its|its)$", "PRP$"), # possessive
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(r"(my|Your|your|Yours|yours)$", "PRP$"), # possessive
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(r"(on|On|in|In|at|At|since|Since)$", "IN"), # time prepopsitions
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(r"(for|For|ago|Ago|before|Before)$", "IN"), # time prepopsitions
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(r"(till|Till|until|Until)$", "IN"), # time prepopsitions
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(r"(by|By|beside|Beside)$", "IN"), # space prepopsitions
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(r"(under|Under|below|Below)$", "IN"), # space prepopsitions
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(r"(over|Over|above|Above)$", "IN"), # space prepopsitions
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(r"(across|Across|through|Through)$", "IN"), # space prepopsitions
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(r"(into|Into|towards|Towards)$", "IN"), # space prepopsitions
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(r"(onto|Onto|from|From)$", "IN"), # space prepopsitions
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(r".*able$", "JJ"), # adjectives
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(r".*ness$", "NN"), # nouns formed from adjectives
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(r".*ly$", "RB"), # adverbs
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(r".*s$", "NNS"), # plural nouns
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(r".*ing$", "VBG"), # gerunds
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(r".*ed$", "VBD"), # past tense verbs
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(r".*", "NN"), # nouns (default)
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]
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)
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return _tagger.tag
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def find_maltparser(parser_dirname):
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"""
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A module to find MaltParser .jar file and its dependencies.
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"""
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if os.path.exists(parser_dirname): # If a full path is given.
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_malt_dir = parser_dirname
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else: # Try to find path to maltparser directory in environment variables.
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_malt_dir = find_dir(parser_dirname, env_vars=("MALT_PARSER",))
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# Checks that that the found directory contains all the necessary .jar
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malt_dependencies = ["", "", ""]
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_malt_jars = set(find_jars_within_path(_malt_dir))
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_jars = {os.path.split(jar)[1] for jar in _malt_jars}
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malt_dependencies = {"log4j.jar", "libsvm.jar", "liblinear-1.8.jar"}
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assert malt_dependencies.issubset(_jars)
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assert any(
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filter(lambda i: i.startswith("maltparser-") and i.endswith(".jar"), _jars)
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)
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return list(_malt_jars)
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def find_malt_model(model_filename):
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"""
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A module to find pre-trained MaltParser model.
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"""
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if model_filename is None:
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return "malt_temp.mco"
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elif os.path.exists(model_filename): # If a full path is given.
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return model_filename
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else: # Try to find path to malt model in environment variables.
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return find_file(model_filename, env_vars=("MALT_MODEL",), verbose=False)
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class MaltParser(ParserI):
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"""
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A class for dependency parsing with MaltParser. The input is the paths to:
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- (optionally) a maltparser directory
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- (optionally) the path to a pre-trained MaltParser .mco model file
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- (optionally) the tagger to use for POS tagging before parsing
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- (optionally) additional Java arguments
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Example:
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>>> from nltk.parse import malt
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>>> # With MALT_PARSER and MALT_MODEL environment set.
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>>> mp = malt.MaltParser(model_filename='engmalt.linear-1.7.mco') # doctest: +SKIP
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>>> mp.parse_one('I shot an elephant in my pajamas .'.split()).tree() # doctest: +SKIP
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(shot I (elephant an) (in (pajamas my)) .)
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>>> # Without MALT_PARSER and MALT_MODEL environment.
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>>> mp = malt.MaltParser('/home/user/maltparser-1.9.2/', '/home/user/engmalt.linear-1.7.mco') # doctest: +SKIP
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>>> mp.parse_one('I shot an elephant in my pajamas .'.split()).tree() # doctest: +SKIP
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(shot I (elephant an) (in (pajamas my)) .)
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"""
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def __init__(
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self,
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parser_dirname="",
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model_filename=None,
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tagger=None,
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additional_java_args=None,
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):
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"""
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An interface for parsing with the Malt Parser.
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:param parser_dirname: The path to the maltparser directory that
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contains the maltparser-1.x.jar
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:type parser_dirname: str
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:param model_filename: The name of the pre-trained model with .mco file
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extension. If provided, training will not be required.
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(see http://www.maltparser.org/mco/mco.html and
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see http://www.patful.com/chalk/node/185)
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:type model_filename: str
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:param tagger: The tagger used to POS tag the raw string before
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formatting to CONLL format. It should behave like `nltk.pos_tag`
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:type tagger: function
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:param additional_java_args: This is the additional Java arguments that
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one can use when calling Maltparser, usually this is the heapsize
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limits, e.g. `additional_java_args=['-Xmx1024m']`
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(see https://goo.gl/mpDBvQ)
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:type additional_java_args: list
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"""
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# Find all the necessary jar files for MaltParser.
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self.malt_jars = find_maltparser(parser_dirname)
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# Initialize additional java arguments.
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self.additional_java_args = (
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additional_java_args if additional_java_args is not None else []
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)
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# Initialize model.
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self.model = find_malt_model(model_filename)
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self._trained = self.model != "malt_temp.mco"
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# Set the working_dir parameters i.e. `-w` from MaltParser's option.
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self.working_dir = tempfile.gettempdir()
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# Initialize POS tagger.
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self.tagger = tagger if tagger is not None else malt_regex_tagger()
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def parse_tagged_sents(self, sentences, verbose=False, top_relation_label="null"):
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"""
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Use MaltParser to parse multiple POS tagged sentences. Takes multiple
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sentences where each sentence is a list of (word, tag) tuples.
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The sentences must have already been tokenized and tagged.
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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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:return: iter(iter(``DependencyGraph``)) the dependency graph
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representation of each sentence
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"""
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if not self._trained:
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raise Exception("Parser has not been trained. Call train() first.")
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with tempfile.NamedTemporaryFile(
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prefix="malt_input.conll.", dir=self.working_dir, mode="w", delete=False
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) as input_file:
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with tempfile.NamedTemporaryFile(
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prefix="malt_output.conll.",
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dir=self.working_dir,
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mode="w",
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delete=False,
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) as output_file:
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# Convert list of sentences to CONLL format.
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for line in taggedsents_to_conll(sentences):
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input_file.write(str(line))
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input_file.close()
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# Generate command to run maltparser.
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cmd = self.generate_malt_command(
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input_file.name, output_file.name, mode="parse"
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)
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# This is a maltparser quirk, it needs to be run
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# where the model file is. otherwise it goes into an awkward
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# missing .jars or strange -w working_dir problem.
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_current_path = os.getcwd() # Remembers the current path.
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try: # Change to modelfile path
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os.chdir(os.path.split(self.model)[0])
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except:
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pass
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ret = self._execute(cmd, verbose) # Run command.
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os.chdir(_current_path) # Change back to current path.
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if ret != 0:
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raise Exception(
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"MaltParser parsing (%s) failed with exit "
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"code %d" % (" ".join(cmd), ret)
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)
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# Must return iter(iter(Tree))
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with open(output_file.name) as infile:
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for tree_str in infile.read().split("\n\n"):
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yield (
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iter(
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[
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DependencyGraph(
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tree_str, top_relation_label=top_relation_label
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)
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]
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)
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)
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os.remove(input_file.name)
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os.remove(output_file.name)
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def parse_sents(self, sentences, verbose=False, top_relation_label="null"):
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"""
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Use MaltParser to parse multiple sentences.
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Takes a list of sentences, where each sentence is a list of words.
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Each sentence will be automatically tagged with this
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MaltParser instance's tagger.
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:param sentences: Input sentences to parse
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:type sentence: list(list(str))
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:return: iter(DependencyGraph)
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"""
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tagged_sentences = (self.tagger(sentence) for sentence in sentences)
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return self.parse_tagged_sents(
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tagged_sentences, verbose, top_relation_label=top_relation_label
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)
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def generate_malt_command(self, inputfilename, outputfilename=None, mode=None):
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"""
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This function generates the maltparser command use at the terminal.
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:param inputfilename: path to the input file
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:type inputfilename: str
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:param outputfilename: path to the output file
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:type outputfilename: str
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"""
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cmd = ["java"]
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cmd += self.additional_java_args # Adds additional java arguments
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# Joins classpaths with ";" if on Windows and on Linux/Mac use ":"
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classpaths_separator = ";" if sys.platform.startswith("win") else ":"
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cmd += [
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"-cp",
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classpaths_separator.join(self.malt_jars),
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] # Adds classpaths for jars
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cmd += ["org.maltparser.Malt"] # Adds the main function.
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# Adds the model file.
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if os.path.exists(self.model): # when parsing
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cmd += ["-c", os.path.split(self.model)[-1]]
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else: # when learning
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cmd += ["-c", self.model]
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cmd += ["-i", inputfilename]
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if mode == "parse":
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cmd += ["-o", outputfilename]
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cmd += ["-m", mode] # mode use to generate parses.
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return cmd
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@staticmethod
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def _execute(cmd, verbose=False):
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output = None if verbose else subprocess.PIPE
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p = subprocess.Popen(cmd, stdout=output, stderr=output)
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return p.wait()
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def train(self, depgraphs, verbose=False):
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"""
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Train MaltParser from a list of ``DependencyGraph`` objects
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:param depgraphs: list of ``DependencyGraph`` objects for training input data
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:type depgraphs: DependencyGraph
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"""
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# Write the conll_str to malt_train.conll file in /tmp/
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with tempfile.NamedTemporaryFile(
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prefix="malt_train.conll.", dir=self.working_dir, mode="w", delete=False
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) as input_file:
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input_str = "\n".join(dg.to_conll(10) for dg in depgraphs)
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input_file.write(str(input_str))
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# Trains the model with the malt_train.conll
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self.train_from_file(input_file.name, verbose=verbose)
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# Removes the malt_train.conll once training finishes.
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os.remove(input_file.name)
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def train_from_file(self, conll_file, verbose=False):
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"""
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Train MaltParser from a file
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:param conll_file: str for the filename of the training input data
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:type conll_file: str
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"""
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# If conll_file is a ZipFilePathPointer,
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# then we need to do some extra massaging
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if isinstance(conll_file, ZipFilePathPointer):
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with tempfile.NamedTemporaryFile(
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prefix="malt_train.conll.", dir=self.working_dir, mode="w", delete=False
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) as input_file:
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with conll_file.open() as conll_input_file:
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conll_str = conll_input_file.read()
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input_file.write(str(conll_str))
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return self.train_from_file(input_file.name, verbose=verbose)
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# Generate command to run maltparser.
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cmd = self.generate_malt_command(conll_file, mode="learn")
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ret = self._execute(cmd, verbose)
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if ret != 0:
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raise Exception(
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"MaltParser training (%s) failed with exit "
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"code %d" % (" ".join(cmd), ret)
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)
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self._trained = True
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if __name__ == "__main__":
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"""
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A demonstration function to show how NLTK users can use the malt parser API.
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>>> from nltk import pos_tag
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>>> assert 'MALT_PARSER' in os.environ, str(
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... "Please set MALT_PARSER in your global environment, e.g.:\n"
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... "$ export MALT_PARSER='/home/user/maltparser-1.9.2/'")
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>>>
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>>> assert 'MALT_MODEL' in os.environ, str(
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... "Please set MALT_MODEL in your global environment, e.g.:\n"
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... "$ export MALT_MODEL='/home/user/engmalt.linear-1.7.mco'")
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>>>
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>>> _dg1_str = str("1 John _ NNP _ _ 2 SUBJ _ _\n"
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... "2 sees _ VB _ _ 0 ROOT _ _\n"
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... "3 a _ DT _ _ 4 SPEC _ _\n"
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... "4 dog _ NN _ _ 2 OBJ _ _\n"
|
||
|
|
... "5 . _ . _ _ 2 PUNCT _ _\n")
|
||
|
|
>>>
|
||
|
|
>>>
|
||
|
|
>>> _dg2_str = str("1 John _ NNP _ _ 2 SUBJ _ _\n"
|
||
|
|
... "2 walks _ VB _ _ 0 ROOT _ _\n"
|
||
|
|
... "3 . _ . _ _ 2 PUNCT _ _\n")
|
||
|
|
>>> dg1 = DependencyGraph(_dg1_str)
|
||
|
|
>>> dg2 = DependencyGraph(_dg2_str)
|
||
|
|
>>> # Initialize a MaltParser object
|
||
|
|
>>> mp = MaltParser()
|
||
|
|
>>>
|
||
|
|
>>> # Trains a model.
|
||
|
|
>>> mp.train([dg1,dg2], verbose=False)
|
||
|
|
>>> sent1 = ['John','sees','Mary', '.']
|
||
|
|
>>> sent2 = ['John', 'walks', 'a', 'dog', '.']
|
||
|
|
>>>
|
||
|
|
>>> # Parse a single sentence.
|
||
|
|
>>> parsed_sent1 = mp.parse_one(sent1)
|
||
|
|
>>> parsed_sent2 = mp.parse_one(sent2)
|
||
|
|
>>> print(parsed_sent1.tree())
|
||
|
|
(sees John Mary .)
|
||
|
|
>>> print(parsed_sent2.tree())
|
||
|
|
(walks John (dog a) .)
|
||
|
|
>>>
|
||
|
|
>>> # Parsing multiple sentences.
|
||
|
|
>>> sentences = [sent1,sent2]
|
||
|
|
>>> parsed_sents = mp.parse_sents(sentences)
|
||
|
|
>>> print(next(next(parsed_sents)).tree())
|
||
|
|
(sees John Mary .)
|
||
|
|
>>> print(next(next(parsed_sents)).tree())
|
||
|
|
(walks John (dog a) .)
|
||
|
|
>>>
|
||
|
|
>>> # Initialize a MaltParser object with an English pre-trained model.
|
||
|
|
>>> parser_dirname = 'maltparser-1.9.2'
|
||
|
|
>>> model_name = 'engmalt.linear-1.7.mco'
|
||
|
|
>>> mp = MaltParser(parser_dirname=parser_dirname, model_filename=model_name, tagger=pos_tag)
|
||
|
|
>>> sent1 = 'I shot an elephant in my pajamas .'.split()
|
||
|
|
>>> sent2 = 'Time flies like banana .'.split()
|
||
|
|
>>> # Parse a single sentence.
|
||
|
|
>>> print(mp.parse_one(sent1).tree())
|
||
|
|
(shot I (elephant an) (in (pajamas my)) .)
|
||
|
|
# Parsing multiple sentences
|
||
|
|
>>> sentences = [sent1,sent2]
|
||
|
|
>>> parsed_sents = mp.parse_sents(sentences)
|
||
|
|
>>> print(next(next(parsed_sents)).tree())
|
||
|
|
(shot I (elephant an) (in (pajamas my)) .)
|
||
|
|
>>> print(next(next(parsed_sents)).tree())
|
||
|
|
(flies Time (like banana) .)
|
||
|
|
"""
|
||
|
|
|
||
|
|
import doctest
|
||
|
|
|
||
|
|
doctest.testmod()
|