# Natural Language Toolkit: Senna POS Tagger
#
# Copyright (C) 2001-2023 NLTK Project
# Author: Rami Al-Rfou' <ralrfou@cs.stonybrook.edu>
# URL: <https://www.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)
Note: Unit tests for this module can be found in test/unit/test_senna.py
>>> from nltk.tag import SennaTagger
>>> tagger = SennaTagger('/usr/share/senna-v3.0') # doctest: +SKIP
>>> tagger.tag('What is the airspeed of an unladen swallow ?'.split()) # doctest: +SKIP
[('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-v3.0') # doctest: +SKIP
>>> chktagger.tag('What is the airspeed of an unladen swallow ?'.split()) # doctest: +SKIP
[('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-v3.0') # doctest: +SKIP
>>> nertagger.tag('Shakespeare theatre was in London .'.split()) # doctest: +SKIP
[('Shakespeare', 'B-PER'), ('theatre', 'O'), ('was', 'O'), ('in', 'O'),
('London', 'B-LOC'), ('.', 'O')]
>>> nertagger.tag('UN headquarters are in NY , USA .'.split()) # doctest: +SKIP
[('UN', 'B-ORG'), ('headquarters', 'O'), ('are', 'O'), ('in', 'O'),
('NY', 'B-LOC'), (',', 'O'), ('USA', 'B-LOC'), ('.', 'O')]
"""
from nltk.classify import Senna
[docs]class SennaTagger(Senna):
[docs] def __init__(self, path, encoding="utf-8"):
super().__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().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
[docs]class SennaChunkTagger(Senna):
[docs] def __init__(self, path, encoding="utf-8"):
super().__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().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-v3.0') # doctest: +SKIP
>>> sent = 'What is the airspeed of an unladen swallow ?'.split()
>>> tagged_sent = chktagger.tag(sent) # doctest: +SKIP
>>> tagged_sent # doctest: +SKIP
[('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')) # doctest: +SKIP
[('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))
[docs]class SennaNERTagger(Senna):
[docs] def __init__(self, path, encoding="utf-8"):
super().__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().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