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243 lines
8.0 KiB
Python
243 lines
8.0 KiB
Python
#### PATTERN | XX ##################################################################################
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# -*- coding: utf-8 -*-
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# Copyright (c) year, institute, country
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# Author: Name (e-mail)
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# License: BSD (see LICENSE.txt for details).
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# http://www.clips.ua.ac.be/pages/pattern
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####################################################################################################
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# Template for pattern.xx, bundling natural language processing tools for language XXXXX.
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# The module bundles a shallow parser (part-of-speech tagger, chunker, lemmatizer)
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# with functions for word inflection (singularization, pluralization, conjugation)
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# and sentiment analysis.
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# Base classes for the parser, verb table and sentiment lexicon are inherited from pattern.text.
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# The parser can be subclassed with a custom tokenizer (finds sentence boundaries)
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# and lemmatizer (uses word inflection to find the base form of words).
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# The part-of-speech tagger requires a lexicon of tagged known words and rules for unknown words.
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# Tools for word inflection should be bundled in pattern.text.xx.inflect.
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from __future__ import unicode_literals
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from __future__ import print_function
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from __future__ import division
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from builtins import str, bytes, dict, int
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from builtins import map, zip, filter
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from builtins import object, range
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import os
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import sys
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try:
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MODULE = os.path.dirname(os.path.realpath(__file__))
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except:
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MODULE = ""
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sys.path.insert(0, os.path.join(MODULE, "..", "..", "..", ".."))
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# Import parser base classes.
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from pattern.text import (
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Lexicon, Model, Morphology, Context, Parser as _Parser, ngrams, pprint, commandline,
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PUNCTUATION
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)
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# Import parse tree base classes.
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from pattern.text.tree import (
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Tree, Text, Sentence, Slice, Chunk, PNPChunk, Chink, Word, table,
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SLASH, WORD, POS, CHUNK, PNP, REL, ANCHOR, LEMMA, AND, OR
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)
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# Import sentiment analysis base classes.
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from pattern.text import (
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Sentiment,
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NOUN, VERB, ADJECTIVE, ADVERB,
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MOOD, IRONY
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)
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# Import spelling base class.
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from pattern.text import (
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Spelling
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)
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# Import verb tenses.
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from pattern.text import (
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INFINITIVE, PRESENT, PAST, FUTURE,
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FIRST, SECOND, THIRD,
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SINGULAR, PLURAL, SG, PL,
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PROGRESSIVE,
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PARTICIPLE
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)
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# Import inflection functions.
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from pattern.text.xx.inflect import (
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article, referenced, DEFINITE, INDEFINITE,
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pluralize, singularize, NOUN, VERB, ADJECTIVE,
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verbs, conjugate, lemma, lexeme, tenses,
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predicative, attributive
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)
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# Import all submodules.
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from pattern.text.xx import inflect
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sys.path.pop(0)
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#--- PARSER ----------------------------------------------------------------------------------------
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# Pattern uses the Penn Treebank II tagset (http://www.clips.ua.ac.be/pages/penn-treebank-tagset).
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# The lexicon for pattern.xx may be using a different tagset (e.g., PAROLE, WOTAN).
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# The following functions are meant to map the tags to Penn Treebank II, see Parser.find_chunks().
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TAGSET = {"??": "NN"} # pattern.xx tagset => Penn Treebank II.
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def tagset2penntreebank(tag):
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return TAGSET.get(tag, tag)
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# Different languages have different contractions (e.g., English "I've" or French "j'ai")
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# and abbreviations. The following functions define contractions and abbreviations
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# for pattern.xx, see also Parser.find_tokens().
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REPLACEMENTS = {"'s": " 's", "'ve": " 've"}
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ABBREVIATIONS = set(("e.g.", "etc.", "i.e."))
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# A lemmatizer can be constructed if we have a pattern.xx.inflect,
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# with functions for noun singularization and verb conjugation (i.e., infinitives).
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def find_lemmata(tokens):
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""" Annotates the tokens with lemmata for plural nouns and conjugated verbs,
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where each token is a [word, part-of-speech] list.
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"""
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for token in tokens:
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word, pos, lemma = token[0], token[1], token[0]
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if pos.startswith("JJ"):
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lemma = predicative(word)
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if pos == "NNS":
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lemma = singularize(word)
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if pos.startswith(("VB", "MD")):
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lemma = conjugate(word, INFINITIVE) or word
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token.append(lemma.lower())
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return tokens
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# Subclass the base parser with the language-specific functionality:
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class Parser(_Parser):
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def find_tokens(self, tokens, **kwargs):
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kwargs.setdefault("abbreviations", ABBREVIATIONS)
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kwargs.setdefault("replace", REPLACEMENTS)
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return _Parser.find_tokens(self, tokens, **kwargs)
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def find_tags(self, tokens, **kwargs):
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kwargs.setdefault("map", tagset2penntreebank)
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return _Parser.find_tags(self, tokens, **kwargs)
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def find_chunks(self, tokens, **kwargs):
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return _Parser.find_chunks(self, tokens, **kwargs)
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def find_lemmata(self, tokens, **kwargs):
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return find_lemmata(tokens)
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# The parser's part-of-speech tagger requires a lexicon of tagged known words,
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# and rules for unknown words. See pattern.text.Morphology and pattern.text.Context
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# for further details. A tutorial on how to acquire data for the lexicon is here:
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# http://www.clips.ua.ac.be/pages/using-wiktionary-to-build-an-italian-part-of-speech-tagger
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# Create the parser with default tags for unknown words:
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# (noun, proper noun, numeric).
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parser = Parser(
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lexicon = os.path.join(MODULE, "xx-lexicon.txt"), # A dict of known words => most frequent tag.
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frequency = os.path.join(MODULE, "xx-frequency.txt"), # A dict of word frequency.
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morphology = os.path.join(MODULE, "xx-morphology.txt"), # A set of suffix rules.
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context = os.path.join(MODULE, "xx-context.txt"), # A set of contextual rules.
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entities = os.path.join(MODULE, "xx-entities.txt"), # A dict of named entities: John = NNP-PERS.
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default = ("NN", "NNP", "CD"),
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language = "xx"
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)
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lexicon = parser.lexicon # Expose lexicon.
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# Create the sentiment lexicon,
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# see pattern/text/xx/xx-sentiment.xml for further details.
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# We also need to define the tag for modifiers,
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# words that modify the score of the following word
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# (e.g., *very* good, *not good, ...)
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sentiment = Sentiment(
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path = os.path.join(MODULE, "xx-sentiment.xml"),
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synset = None,
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negations = ("no", "not", "never"),
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modifiers = ("RB",),
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modifier = lambda w: w.endswith("ly"), # brilliantly, hardly, partially, ...
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language = "xx"
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)
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# Nothing should be changed below.
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def tokenize(s, *args, **kwargs):
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""" Returns a list of sentences, where punctuation marks have been split from words.
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"""
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return parser.find_tokens(s, *args, **kwargs)
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def parse(s, *args, **kwargs):
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""" Returns a tagged Unicode string.
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"""
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return parser.parse(s, *args, **kwargs)
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def parsetree(s, *args, **kwargs):
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""" Returns a parsed Text from the given string.
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"""
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return Text(parse(s, *args, **kwargs))
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def tree(s, token=[WORD, POS, CHUNK, PNP, REL, LEMMA]):
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""" Returns a parsed Text from the given parsed string.
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"""
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return Text(s, token)
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def tag(s, tokenize=True, encoding="utf-8", **kwargs):
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""" Returns a list of (token, tag)-tuples from the given string.
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"""
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tags = []
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for sentence in parse(s, tokenize, True, False, False, False, encoding, **kwargs).split():
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for token in sentence:
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tags.append((token[0], token[1]))
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return tags
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def keywords(s, top=10, **kwargs):
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""" Returns a sorted list of keywords in the given string.
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"""
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return parser.find_keywords(s, **dict({
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"frequency": parser.frequency,
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"top": top,
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"pos": ("NN",),
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"ignore": ("rt",)}, **kwargs))
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def polarity(s, **kwargs):
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""" Returns the sentence polarity (positive/negative) between -1.0 and 1.0.
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"""
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return sentiment(s, **kwargs)[0]
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def subjectivity(s, **kwargs):
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""" Returns the sentence subjectivity (objective/subjective) between 0.0 and 1.0.
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"""
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return sentiment(s, **kwargs)[1]
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def positive(s, threshold=0.1, **kwargs):
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""" Returns True if the given sentence has a positive sentiment.
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"""
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return polarity(s, **kwargs) >= threshold
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split = tree # Backwards compatibility.
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#---------------------------------------------------------------------------------------------------
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# python -m pattern.xx xml -s "..." -OTCL
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if __name__ == "__main__":
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commandline(parse)
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