# -*- coding: utf-8 -*-
# SPDX-FileCopyrightText: 2016-2024 PyThaiNLP Project
# SPDX-License-Identifier: Apache-2.0
"""
Segmenting text into Enhanced Thai Character Clusters (ETCCs)
Python implementation by Wannaphong Phatthiyaphaibun
This implementation relies on a dictionary of ETCC created from etcc.txt
in pythainlp/corpus.
Notebook:
https://colab.research.google.com/drive/1UTQgxxMRxOr9Jp1B1jcq1frBNvorhtBQ
:See Also:
Jeeragone Inrut, Patiroop Yuanghirun, Sarayut Paludkong, Supot Nitsuwat, and
Para Limmaneepraserth. "Thai word segmentation using combination of forward
and backward longest matching techniques." In International Symposium on
Communications and Information Technology (ISCIT), pp. 37-40. 2001.
"""
import re
from typing import List
from pythainlp import thai_follow_vowels
from pythainlp.corpus import get_corpus
from pythainlp.tokenize import Tokenizer
_cut_etcc = Tokenizer(get_corpus("etcc.txt"), engine="longest")
_PAT_ENDING_CHAR = f"[{thai_follow_vowels}ๆฯ]"
_RE_ENDING_CHAR = re.compile(_PAT_ENDING_CHAR)
def _cut_subword(tokens: List[str]) -> List[str]:
len_tokens = len(tokens)
i = 0
while True:
if i == len_tokens:
break
if _RE_ENDING_CHAR.search(tokens[i]) and i > 0 and len(tokens[i]) == 1:
tokens[i - 1] += tokens[i]
del tokens[i]
len_tokens -= 1
i += 1
return tokens
[docs]
def segment(text: str) -> List[str]:
"""
Segmenting text into ETCCs.
Enhanced Thai Character Cluster (ETCC) is a kind of subword unit.
The concept was presented in Inrut, Jeeragone, Patiroop Yuanghirun,
Sarayut Paludkong, Supot Nitsuwat, and Para Limmaneepraserth.
"Thai word segmentation using combination of forward and backward
longest matching techniques." In International Symposium on Communications
and Information Technology (ISCIT), pp. 37-40. 2001.
:param str text: text to be tokenized into character clusters
:return: list of clusters, tokenized from the text
:return: List[str]
"""
if not text or not isinstance(text, str):
return []
return _cut_subword(_cut_etcc.word_tokenize(text))