Source code for pythainlp.tag.pos_tag

# -*- coding: utf-8 -*-
from typing import List, Tuple


[docs]def pos_tag( words: List[str], engine: str = "perceptron", corpus: str = "orchid" ) -> List[Tuple[str, str]]: """ Marks words with part-of-speech (POS) tags, such as 'NOUN' and 'VERB'. :param list words: a list of tokenized words :param str engine: * *perceptron* - perceptron tagger (default) * *unigram* - unigram tagger :param str corpus: the corpus that used to create the language model for tagger * *lst20* - `LST20 <https://aiforthai.in.th/corpus.php>`_ corpus \ by National Electronics and Computer Technology Center, Thailand * *lst20_ud* - LST20 text, with tags mapped to Universal POS tags \ from `Universal Dependencies <https://universaldependencies.org/>` * *orchid* - `ORCHID \ <https://www.academia.edu/9127599/Thai_Treebank>`_ corpus, \ text from Thai academic articles (default) * *orchid_ud* - ORCHID text, with tags mapped to Universal POS tags * *pud* - `Parallel Universal Dependencies (PUD)\ <https://github.com/UniversalDependencies/UD_Thai-PUD>`_ \ treebanks, natively use Universal POS tags :return: a list of tuples (word, POS tag) :rtype: list[tuple[str, str]] :Example: Tag words with corpus `orchid` (default):: from pythainlp.tag import pos_tag words = ['ฉัน','มี','ชีวิต','รอด','ใน','อาคาร','หลบภัย','ของ', \\ 'นายก', 'เชอร์ชิล'] pos_tag(words) # output: # [('ฉัน', 'PPRS'), ('มี', 'VSTA'), ('ชีวิต', 'NCMN'), ('รอด', 'NCMN'), # ('ใน', 'RPRE'), ('อาคาร', 'NCMN'), ('หลบภัย', 'NCMN'), # ('ของ', 'RPRE'), ('นายก', 'NCMN'), ('เชอร์ชิล', 'NCMN')] Tag words with corpus `orchid_ud`:: from pythainlp.tag import pos_tag words = ['ฉัน','มี','ชีวิต','รอด','ใน','อาคาร','หลบภัย','ของ', \\ 'นายก', 'เชอร์ชิล'] pos_tag(words, corpus='orchid_ud') # output: # [('ฉัน', 'PROPN'), ('มี', 'VERB'), ('ชีวิต', 'NOUN'), # ('รอด', 'NOUN'), ('ใน', 'ADP'), ('อาคาร', 'NOUN'), # ('หลบภัย', 'NOUN'), ('ของ', 'ADP'), ('นายก', 'NOUN'), # ('เชอร์ชิล', 'NOUN')] Tag words with corpus `pud`:: from pythainlp.tag import pos_tag words = ['ฉัน','มี','ชีวิต','รอด','ใน','อาคาร','หลบภัย','ของ', \\ 'นายก', 'เชอร์ชิล'] pos_tag(words, corpus='pud') # [('ฉัน', 'PRON'), ('มี', 'VERB'), ('ชีวิต', 'NOUN'), ('รอด', 'VERB'), # ('ใน', 'ADP'), ('อาคาร', 'NOUN'), ('หลบภัย', 'NOUN'), # ('ของ', 'ADP'), ('นายก', 'NOUN'), ('เชอร์ชิล', 'PROPN')] Tag words with different engines including *perceptron* and *unigram*:: from pythainlp.tag import pos_tag words = ['เก้าอี้','มี','จำนวน','ขา', ' ', '=', '3'] pos_tag(words, engine='perceptron', corpus='orchid') # output: # [('เก้าอี้', 'NCMN'), ('มี', 'VSTA'), ('จำนวน', 'NCMN'), # ('ขา', 'NCMN'), (' ', 'PUNC'), # ('=', 'PUNC'), ('3', 'NCNM')] pos_tag(words, engine='unigram', corpus='pud') # output: # [('เก้าอี้', None), ('มี', 'VERB'), ('จำนวน', 'NOUN'), ('ขา', None), # ('<space>', None), ('<equal>', None), ('3', 'NUM')] """ if not words: return [] if engine == "perceptron": from .perceptron import tag as tag_ else: # default, use "unigram" ("old") engine from .unigram import tag as tag_ word_tags = tag_(words, corpus=corpus) return word_tags
[docs]def pos_tag_sents( sentences: List[List[str]], engine: str = "perceptron", corpus: str = "orchid", ) -> List[List[Tuple[str, str]]]: """ Marks sentences with part-of-speech (POS) tags. :param list sentences: a list of lists of tokenized words :param str engine: * *perceptron* - perceptron tagger (default) * *unigram* - unigram tagger :param str corpus: the corpus that used to create the language model for tagger * *lst20* - `LST20 <https://aiforthai.in.th/corpus.php>`_ corpus \ by National Electronics and Computer Technology Center, Thailand * *lst20_ud* - LST20 text, with tags mapped to Universal POS tags \ from `Universal Dependencies <https://universaldependencies.org/>` * *orchid* - `ORCHID \ <https://www.academia.edu/9127599/Thai_Treebank>`_ corpus, \ text from Thai academic articles (default) * *orchid_ud* - ORCHID text, with tags mapped to Universal POS tags * *pud* - `Parallel Universal Dependencies (PUD)\ <https://github.com/UniversalDependencies/UD_Thai-PUD>`_ \ treebanks, natively use Universal POS tags :return: a list of lists of tuples (word, POS tag) :rtype: list[list[tuple[str, str]]] :Example: Labels POS for two sentences:: from pythainlp.tag import pos_tag_sents sentences = [['เก้าอี้','มี','3','ขา'], \\ ['นก', 'บิน', 'กลับ', 'รัง']] pos_tag_sents(sentences, corpus='pud) # output: # [[('เก้าอี้', 'PROPN'), ('มี', 'VERB'), ('3', 'NUM'), # ('ขา', 'NOUN')], [('นก', 'NOUN'), ('บิน', 'VERB'), # ('กลับ', 'VERB'), ('รัง', 'NOUN')]] """ if not sentences: return [] return [pos_tag(sent, engine=engine, corpus=corpus) for sent in sentences]