# encoding: utf-8
# Natural Language Toolkit: Senna POS Tagger
#
# Copyright (C) 2001-2015 NLTK Project
# Author: Rami Al-Rfou' <ralrfou@cs.stonybrook.edu>
# URL: <http://nltk.org/>
# For license information, see LICENSE.TXT
"""
Senna POS tagger, NER Tagger, Chunk Tagger
The input is:
- path to the directory that contains SENNA executables. If the path is incorrect,
SennaTagger will automatically search for executable file specified in SENNA environment variable
- (optionally) the encoding of the input data (default:utf-8)
>>> from nltk.tag import SennaTagger
>>> tagger = SennaTagger('/usr/share/senna-v2.0')
>>> tagger.tag('What is the airspeed of an unladen swallow ?'.split())
[('What', 'WP'), ('is', 'VBZ'), ('the', 'DT'), ('airspeed', 'NN'),
('of', 'IN'), ('an', 'DT'), ('unladen', 'NN'), ('swallow', 'NN'), ('?', '.')]
>>> from nltk.tag import SennaChunkTagger
>>> chktagger = SennaChunkTagger('/usr/share/senna-v2.0')
>>> chktagger.tag('What is the airspeed of an unladen swallow ?'.split())
[('What', 'B-NP'), ('is', 'B-VP'), ('the', 'B-NP'), ('airspeed', 'I-NP'),
('of', 'B-PP'), ('an', 'B-NP'), ('unladen', 'I-NP'), ('swallow', 'I-NP'),
('?', 'O')]
>>> from nltk.tag import SennaNERTagger
>>> nertagger = SennaNERTagger('/usr/share/senna-v2.0')
>>> nertagger.tag('Shakespeare theatre was in London .'.split())
[('Shakespeare', 'B-PER'), ('theatre', 'O'), ('was', 'O'), ('in', 'O'),
('London', 'B-LOC'), ('.', 'O')]
>>> nertagger.tag('UN headquarters are in NY , USA .'.split())
[('UN', 'B-ORG'), ('headquarters', 'O'), ('are', 'O'), ('in', 'O'),
('NY', 'B-LOC'), (',', 'O'), ('USA', 'B-LOC'), ('.', 'O')]
"""
from nltk.compat import python_2_unicode_compatible
from nltk.classify import Senna
@python_2_unicode_compatible
[docs]class SennaTagger(Senna):
[docs] def __init__(self, path, encoding='utf-8'):
super(SennaTagger, self).__init__(path, ['pos'], encoding)
[docs] def tag_sents(self, sentences):
"""
Applies the tag method over a list of sentences. This method will return
for each sentence a list of tuples of (word, tag).
"""
tagged_sents = super(SennaTagger, self).tag_sents(sentences)
for i in range(len(tagged_sents)):
for j in range(len(tagged_sents[i])):
annotations = tagged_sents[i][j]
tagged_sents[i][j] = (annotations['word'], annotations['pos'])
return tagged_sents
@python_2_unicode_compatible
[docs]class SennaChunkTagger(Senna):
[docs] def __init__(self, path, encoding='utf-8'):
super(SennaChunkTagger, self).__init__(path, ['chk'], encoding)
[docs] def tag_sents(self, sentences):
"""
Applies the tag method over a list of sentences. This method will return
for each sentence a list of tuples of (word, tag).
"""
tagged_sents = super(SennaChunkTagger, self).tag_sents(sentences)
for i in range(len(tagged_sents)):
for j in range(len(tagged_sents[i])):
annotations = tagged_sents[i][j]
tagged_sents[i][j] = (annotations['word'], annotations['chk'])
return tagged_sents
[docs] def bio_to_chunks(self, tagged_sent, chunk_type):
"""
Extracts the chunks in a BIO chunk-tagged sentence.
>>> from nltk.tag import SennaChunkTagger
>>> chktagger = SennaChunkTagger('/usr/share/senna-v2.0')
>>> sent = 'What is the airspeed of an unladen swallow ?'.split()
>>> tagged_sent = chktagger.tag(sent)
>>> tagged_sent
[('What', 'B-NP'), ('is', 'B-VP'), ('the', 'B-NP'), ('airspeed', 'I-NP'),
('of', 'B-PP'), ('an', 'B-NP'), ('unladen', 'I-NP'), ('swallow', 'I-NP'),
('?', 'O')]
>>> list(chktagger.bio_to_chunks(tagged_sent, chunk_type='NP'))
[('What', '0'), ('the airspeed', '2-3'), ('an unladen swallow', '5-6-7')]
:param tagged_sent: A list of tuples of word and BIO chunk tag.
:type tagged_sent: list(tuple)
:param tagged_sent: The chunk tag that users want to extract, e.g. 'NP' or 'VP'
:type tagged_sent: str
:return: An iterable of tuples of chunks that users want to extract
and their corresponding indices.
:rtype: iter(tuple(str))
"""
current_chunk = []
current_chunk_position = []
for idx, word_pos in enumerate(tagged_sent):
word, pos = word_pos
if '-'+chunk_type in pos: # Append the word to the current_chunk.
current_chunk.append((word))
current_chunk_position.append((idx))
else:
if current_chunk: # Flush the full chunk when out of an NP.
_chunk_str = ' '.join(current_chunk)
_chunk_pos_str = '-'.join(map(str, current_chunk_position))
yield _chunk_str, _chunk_pos_str
current_chunk = []
current_chunk_position = []
if current_chunk: # Flush the last chunk.
yield ' '.join(current_chunk), '-'.join(map(str, current_chunk_position))
@python_2_unicode_compatible
[docs]class SennaNERTagger(Senna):
[docs] def __init__(self, path, encoding='utf-8'):
super(SennaNERTagger, self).__init__(path, ['ner'], encoding)
[docs] def tag_sents(self, sentences):
"""
Applies the tag method over a list of sentences. This method will return
for each sentence a list of tuples of (word, tag).
"""
tagged_sents = super(SennaNERTagger, self).tag_sents(sentences)
for i in range(len(tagged_sents)):
for j in range(len(tagged_sents[i])):
annotations = tagged_sents[i][j]
tagged_sents[i][j] = (annotations['word'], annotations['ner'])
return tagged_sents
# skip doctests if Senna is not installed
def setup_module(module):
from nose import SkipTest
try:
tagger = Senna('/usr/share/senna-v2.0', ['pos', 'chk', 'ner'])
except OSError:
raise SkipTest("Senna executable not found")