Source code for pythainlp.tag.named_entity
# -*- coding: utf-8 -*-
"""
Named-entity recognizer
"""
__all__ = ["ThaiNameTagger"]
from typing import Dict, List, Tuple, Union
from pycrfsuite import Tagger as CRFTagger
from pythainlp.corpus import get_corpus_path, thai_stopwords
from pythainlp.tag import pos_tag
from pythainlp.tokenize import word_tokenize
from pythainlp.util import isthai
_CORPUS_NAME = "thainer"
_TOKENIZER_ENGINE = "newmm" # should be the same as one used in training data
def _is_stopword(word: str) -> bool: # เช็คว่าเป็นคำฟุ่มเฟือย
return word in thai_stopwords()
def _doc2features(doc, i) -> Dict:
word = doc[i][0]
postag = doc[i][1]
# Features from current word
features = {
"word.word": word,
"word.stopword": _is_stopword(word),
"word.isthai": isthai(word),
"word.isspace": word.isspace(),
"postag": postag,
"word.isdigit": word.isdigit(),
}
if word.isdigit() and len(word) == 5:
features["word.islen5"] = True
# Features from previous word
if i > 0:
prevword = doc[i - 1][0]
prevpostag = doc[i - 1][1]
prev_features = {
"word.prevword": prevword,
"word.previsspace": prevword.isspace(),
"word.previsthai": isthai(prevword),
"word.prevstopword": _is_stopword(prevword),
"word.prevpostag": prevpostag,
"word.prevwordisdigit": prevword.isdigit(),
}
features.update(prev_features)
else:
features["BOS"] = True # Special "Beginning of Sequence" tag
# Features from next word
if i < len(doc) - 1:
nextword = doc[i + 1][0]
nextpostag = doc[i + 1][1]
next_features = {
"word.nextword": nextword,
"word.nextisspace": nextword.isspace(),
"word.nextpostag": nextpostag,
"word.nextisthai": isthai(nextword),
"word.nextstopword": _is_stopword(nextword),
"word.nextwordisdigit": nextword.isdigit(),
}
features.update(next_features)
else:
features["EOS"] = True # Special "End of Sequence" tag
return features
[docs]class ThaiNameTagger:
def __init__(self, version: str = "1.5") -> None:
"""
Thai named-entity recognizer.
:param str version: Thai NER version.
It's support Thai NER 1.4 & 1.5.
The defualt value is `1.5`
"""
self.crf = CRFTagger()
if version == "1.4":
self.crf.open(get_corpus_path("thainer-1.4", version="1.4"))
self.pos_tag_name = "orchid_ud"
else:
self.crf.open(get_corpus_path(_CORPUS_NAME, version="1.5"))
self.pos_tag_name = "lst20"
[docs] def get_ner(
self, text: str, pos: bool = True, tag: bool = False
) -> Union[List[Tuple[str, str]], List[Tuple[str, str, str]]]:
"""
This function tags named-entitiy from text in IOB format.
:param str text: text in Thai to be tagged
:param bool pos: To include POS tags in the results (`True`) or
exclude (`False`). The defualt value is `True`
:param bool tag: output like html tag.
:return: a list of tuple associated with tokenized word, NER tag,
POS tag (if the parameter `pos` is specified as `True`),
and output like html tag (if the parameter `tag` is
specified as `True`).
Otherwise, return a list of tuple associated with tokenized
word and NER tag
:rtype: Union[list[tuple[str, str]], list[tuple[str, str, str]]], str
:Note:
* For the POS tags to be included in the results, this function
uses :func:`pythainlp.tag.pos_tag` with engine as `perceptron`
and corpus as orchid_ud`.
:Example:
>>> from pythainlp.tag.named_entity import ThaiNameTagger
>>>
>>> ner = ThaiNameTagger()
>>> ner.get_ner("วันที่ 15 ก.ย. 61 ทดสอบระบบเวลา 14:49 น.")
[('วันที่', 'NOUN', 'O'), (' ', 'PUNCT', 'O'),
('15', 'NUM', 'B-DATE'), (' ', 'PUNCT', 'I-DATE'),
('ก.ย.', 'NOUN', 'I-DATE'), (' ', 'PUNCT', 'I-DATE'),
('61', 'NUM', 'I-DATE'), (' ', 'PUNCT', 'O'),
('ทดสอบ', 'VERB', 'O'), ('ระบบ', 'NOUN', 'O'),
('เวลา', 'NOUN', 'O'), (' ', 'PUNCT', 'O'),
('14', 'NOUN', 'B-TIME'), (':', 'PUNCT', 'I-TIME'),
('49', 'NUM', 'I-TIME'), (' ', 'PUNCT', 'I-TIME'),
('น.', 'NOUN', 'I-TIME')]
>>>
>>> ner.get_ner("วันที่ 15 ก.ย. 61 ทดสอบระบบเวลา 14:49 น.",
pos=False)
[('วันที่', 'O'), (' ', 'O'),
('15', 'B-DATE'), (' ', 'I-DATE'),
('ก.ย.', 'I-DATE'), (' ', 'I-DATE'),
('61', 'I-DATE'), (' ', 'O'),
('ทดสอบ', 'O'), ('ระบบ', 'O'),
('เวลา', 'O'), (' ', 'O'),
('14', 'B-TIME'), (':', 'I-TIME'),
('49', 'I-TIME'), (' ', 'I-TIME'),
('น.', 'I-TIME')]
>>> ner.get_ner("วันที่ 15 ก.ย. 61 ทดสอบระบบเวลา 14:49 น.",
tag=True)
'วันที่ <DATE>15 ก.ย. 61</DATE> ทดสอบระบบเวลา <TIME>14:49 น.</TIME>'
"""
tokens = word_tokenize(text, engine=_TOKENIZER_ENGINE)
pos_tags = pos_tag(
tokens,
engine="perceptron",
corpus=self.pos_tag_name
)
x_test = ThaiNameTagger.__extract_features(pos_tags)
y = self.crf.tag(x_test)
sent_ner = [(pos_tags[i][0], data) for i, data in enumerate(y)]
if tag:
temp = ""
sent = ""
for idx, (word, ner) in enumerate(sent_ner):
if ner.startswith("B-") and temp != "":
sent += "</" + temp + ">"
temp = ner[2:]
sent += "<" + temp + ">"
elif ner.startswith("B-"):
temp = ner[2:]
sent += "<" + temp + ">"
elif ner == "O" and temp != "":
sent += "</" + temp + ">"
temp = ""
sent += word
if idx == len(sent_ner) - 1 and temp != "":
sent += "</" + temp + ">"
return sent
if pos:
return [
(pos_tags[i][0], pos_tags[i][1], data)
for i, data in enumerate(y)
]
return sent_ner
@staticmethod
def __extract_features(doc):
return [_doc2features(doc, i) for i in range(len(doc))]