# -*- coding: utf-8 -*-
# SPDX-FileCopyrightText: 2016-2025 PyThaiNLP Project
# SPDX-FileType: SOURCE
# SPDX-License-Identifier: Apache-2.0
"""
Named-entity recognizer
"""
from typing import List, Tuple, Union
[docs]
class NER:
"""
Class of named-entity recognizer
:param str engine: engine of named-entity recognizer
:param str corpus: corpus
**Options for engine**
* *thainer-v2* - Thai NER engine v2.0 for Thai NER 2.0 (default)
* *thainer* - Thai NER engine
* *tltk* - wrapper for `TLTK <https://pypi.org/project/tltk/>`_.
**Options for corpus**
* *thainer* - Thai NER corpus (default)
**Note**: The tltk engine supports NER models from tltk only.
"""
[docs]
def __init__(self, engine: str = "thainer-v2", corpus: str = "thainer") -> None:
self.load_engine(engine=engine, corpus=corpus)
[docs]
def load_engine(self, engine: str, corpus: str) -> None:
self.name_engine = engine
self.engine = None
if engine == "thainer" and corpus == "thainer":
from pythainlp.tag.thainer import ThaiNameTagger
self.engine = ThaiNameTagger()
elif engine == "thainer-v2" and corpus == "thainer":
from pythainlp.wangchanberta import NamedEntityRecognition
self.engine = NamedEntityRecognition(model="pythainlp/thainer-corpus-v2-base-model")
elif engine == "tltk":
from pythainlp.tag import tltk
self.engine = tltk
elif engine == "wangchanberta" and corpus == "thainer":
from pythainlp.wangchanberta import ThaiNameTagger
self.engine = ThaiNameTagger(dataset_name=corpus)
elif engine == "phayathaibert" and corpus == "thainer-v2":
from pythainlp.phayathaibert.core import NamedEntityTagger
self.engine = NamedEntityTagger()
else:
raise ValueError(
"NER class not support {0} engine or {1} corpus.".format(
engine, corpus
)
)
[docs]
def tag(self,
text,
pos=False,
tag=False
) -> Union[List[Tuple[str, str]], List[Tuple[str, str, str]], str]:
"""
This function tags named entities in text in IOB format.
:param str text: text in Thai to be tagged
:param bool pos: output with part-of-speech tags.\
(wangchanberta is not supported)
:param bool tag: output HTML-like tags.
:return: a list of tuples associated with tokenized words, NER tags,
POS tags (if the parameter `pos` is specified as `True`),
and output HTML-like tags (if the parameter `tag` is
specified as `True`).
Otherwise, return a list of tuples associated with tokenized
words and NER tags
:rtype: Union[List[Tuple[str, str]], List[Tuple[str, str, str]], str]
:Example:
>>> from pythainlp.tag import NER
>>>
>>> ner = NER("thainer")
>>> ner.tag("ทดสอบ นายวรรณพงษ์ ภัททิยไพบูลย์")
[('ทดสอบ', 'O'),
[(' ', 'O'),
('นาย', 'B-PERSON'),
('วรรณ', 'I-PERSON'),
('พงษ์', 'I-PERSON'),
(' ', 'I-PERSON'),
('ภัททิย', 'I-PERSON'),
('ไพบูลย์', 'I-PERSON')]
>>> ner.tag("ทดสอบ นายวรรณพงษ์ ภัททิยไพบูลย์", tag=True)
'ทดสอบ <PERSON>นายวรรณพงษ์ ภัททิยไพบูลย์</PERSON>'
"""
return self.engine.get_ner(text, tag=tag, pos=pos)
[docs]
class NNER:
"""
Nested Named Entity Recognition
:param str engine: engine of nested named entity recognizer
:param str corpus: corpus
**Options for engine**
* *thai_nner* - Thai NER engine
"""
[docs]
def __init__(self, engine: str = "thai_nner") -> None:
self.load_engine(engine)
[docs]
def load_engine(self, engine: str = "thai_nner") -> None:
from pythainlp.tag.thai_nner import Thai_NNER
self.engine = Thai_NNER()
[docs]
def tag(self, text) -> Tuple[List[str], List[dict]]:
"""
This function tags nested named entities.
:param str text: text in Thai to be tagged
:return: a list of tuples associated with tokenized words and NNER tags.
:rtype: Tuple[List[str], List[dict]]
:Example:
>>> from pythainlp.tag.named_entity import NNER
>>> nner = NNER()
>>> nner.tag("แมวทำอะไรตอนห้าโมงเช้า")
([
'<s>',
'',
'แมว',
'ทํา',
'',
'อะไร',
'ตอน',
'',
'ห้า',
'',
'โมง',
'',
'เช้า',
'</s>'
],
[
{
'text': ['', 'ห้า'],
'span': [7, 9],
'entity_type': 'cardinal'
},
{
'text': ['', 'ห้า', '', 'โมง'],
'span': [7, 11],
'entity_type': 'time'
},
{
'text': ['', 'โมง'],
'span': [9, 11],
'entity_type': 'unit'
}
])
"""
return self.engine.tag(text)