148 lines
6.0 KiB
Python
148 lines
6.0 KiB
Python
# encoding: utf-8
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# Natural Language Toolkit: Senna POS Tagger
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#
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# Copyright (C) 2001-2019 NLTK Project
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# Author: Rami Al-Rfou' <ralrfou@cs.stonybrook.edu>
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# URL: <http://nltk.org/>
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# For license information, see LICENSE.TXT
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"""
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Senna POS tagger, NER Tagger, Chunk Tagger
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The input is:
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- path to the directory that contains SENNA executables. If the path is incorrect,
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SennaTagger will automatically search for executable file specified in SENNA environment variable
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- (optionally) the encoding of the input data (default:utf-8)
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Note: Unit tests for this module can be found in test/unit/test_senna.py
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>>> from nltk.tag import SennaTagger
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>>> tagger = SennaTagger('/usr/share/senna-v3.0')
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>>> tagger.tag('What is the airspeed of an unladen swallow ?'.split()) # doctest: +SKIP
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[('What', 'WP'), ('is', 'VBZ'), ('the', 'DT'), ('airspeed', 'NN'),
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('of', 'IN'), ('an', 'DT'), ('unladen', 'NN'), ('swallow', 'NN'), ('?', '.')]
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>>> from nltk.tag import SennaChunkTagger
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>>> chktagger = SennaChunkTagger('/usr/share/senna-v3.0')
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>>> chktagger.tag('What is the airspeed of an unladen swallow ?'.split()) # doctest: +SKIP
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[('What', 'B-NP'), ('is', 'B-VP'), ('the', 'B-NP'), ('airspeed', 'I-NP'),
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('of', 'B-PP'), ('an', 'B-NP'), ('unladen', 'I-NP'), ('swallow', 'I-NP'),
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('?', 'O')]
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>>> from nltk.tag import SennaNERTagger
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>>> nertagger = SennaNERTagger('/usr/share/senna-v3.0')
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>>> nertagger.tag('Shakespeare theatre was in London .'.split()) # doctest: +SKIP
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[('Shakespeare', 'B-PER'), ('theatre', 'O'), ('was', 'O'), ('in', 'O'),
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('London', 'B-LOC'), ('.', 'O')]
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>>> nertagger.tag('UN headquarters are in NY , USA .'.split()) # doctest: +SKIP
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[('UN', 'B-ORG'), ('headquarters', 'O'), ('are', 'O'), ('in', 'O'),
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('NY', 'B-LOC'), (',', 'O'), ('USA', 'B-LOC'), ('.', 'O')]
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"""
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from nltk.classify import Senna
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class SennaTagger(Senna):
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def __init__(self, path, encoding="utf-8"):
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super(SennaTagger, self).__init__(path, ["pos"], encoding)
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def tag_sents(self, sentences):
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"""
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Applies the tag method over a list of sentences. This method will return
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for each sentence a list of tuples of (word, tag).
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"""
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tagged_sents = super(SennaTagger, self).tag_sents(sentences)
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for i in range(len(tagged_sents)):
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for j in range(len(tagged_sents[i])):
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annotations = tagged_sents[i][j]
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tagged_sents[i][j] = (annotations["word"], annotations["pos"])
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return tagged_sents
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class SennaChunkTagger(Senna):
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def __init__(self, path, encoding="utf-8"):
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super(SennaChunkTagger, self).__init__(path, ["chk"], encoding)
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def tag_sents(self, sentences):
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"""
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Applies the tag method over a list of sentences. This method will return
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for each sentence a list of tuples of (word, tag).
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"""
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tagged_sents = super(SennaChunkTagger, self).tag_sents(sentences)
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for i in range(len(tagged_sents)):
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for j in range(len(tagged_sents[i])):
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annotations = tagged_sents[i][j]
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tagged_sents[i][j] = (annotations["word"], annotations["chk"])
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return tagged_sents
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def bio_to_chunks(self, tagged_sent, chunk_type):
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"""
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Extracts the chunks in a BIO chunk-tagged sentence.
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>>> from nltk.tag import SennaChunkTagger
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>>> chktagger = SennaChunkTagger('/usr/share/senna-v3.0')
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>>> sent = 'What is the airspeed of an unladen swallow ?'.split()
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>>> tagged_sent = chktagger.tag(sent) # doctest: +SKIP
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>>> tagged_sent # doctest: +SKIP
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[('What', 'B-NP'), ('is', 'B-VP'), ('the', 'B-NP'), ('airspeed', 'I-NP'),
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('of', 'B-PP'), ('an', 'B-NP'), ('unladen', 'I-NP'), ('swallow', 'I-NP'),
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('?', 'O')]
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>>> list(chktagger.bio_to_chunks(tagged_sent, chunk_type='NP')) # doctest: +SKIP
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[('What', '0'), ('the airspeed', '2-3'), ('an unladen swallow', '5-6-7')]
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:param tagged_sent: A list of tuples of word and BIO chunk tag.
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:type tagged_sent: list(tuple)
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:param tagged_sent: The chunk tag that users want to extract, e.g. 'NP' or 'VP'
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:type tagged_sent: str
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:return: An iterable of tuples of chunks that users want to extract
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and their corresponding indices.
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:rtype: iter(tuple(str))
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"""
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current_chunk = []
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current_chunk_position = []
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for idx, word_pos in enumerate(tagged_sent):
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word, pos = word_pos
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if "-" + chunk_type in pos: # Append the word to the current_chunk.
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current_chunk.append((word))
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current_chunk_position.append((idx))
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else:
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if current_chunk: # Flush the full chunk when out of an NP.
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_chunk_str = " ".join(current_chunk)
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_chunk_pos_str = "-".join(map(str, current_chunk_position))
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yield _chunk_str, _chunk_pos_str
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current_chunk = []
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current_chunk_position = []
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if current_chunk: # Flush the last chunk.
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yield " ".join(current_chunk), "-".join(map(str, current_chunk_position))
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class SennaNERTagger(Senna):
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def __init__(self, path, encoding="utf-8"):
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super(SennaNERTagger, self).__init__(path, ["ner"], encoding)
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def tag_sents(self, sentences):
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"""
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Applies the tag method over a list of sentences. This method will return
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for each sentence a list of tuples of (word, tag).
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"""
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tagged_sents = super(SennaNERTagger, self).tag_sents(sentences)
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for i in range(len(tagged_sents)):
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for j in range(len(tagged_sents[i])):
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annotations = tagged_sents[i][j]
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tagged_sents[i][j] = (annotations["word"], annotations["ner"])
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return tagged_sents
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# skip doctests if Senna is not installed
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def setup_module(module):
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from nose import SkipTest
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try:
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tagger = Senna("/usr/share/senna-v3.0", ["pos", "chk", "ner"])
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except OSError:
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raise SkipTest("Senna executable not found")
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