| |
| |
| |
| |
| """ |
| nercut 0.2 |
| |
| Dictionary-based maximal matching word segmentation, constrained by |
| Thai Character Cluster (TCC) boundaries, and combining tokens that are |
| parts of the same named entity. |
| |
| Code by Wannaphong Phatthiyaphaibun |
| """ |
| from typing import Iterable, List |
|
|
| from pythainlp.tag.named_entity import NER |
|
|
| _thainer = NER(engine="thainer") |
|
|
|
|
| def segment( |
| text: str, |
| taglist: Iterable[str] = [ |
| "ORGANIZATION", |
| "PERSON", |
| "PHONE", |
| "EMAIL", |
| "DATE", |
| "TIME", |
| ], |
| tagger=_thainer, |
| ) -> List[str]: |
| """ |
| Dictionary-based maximal matching word segmentation, constrained by |
| Thai Character Cluster (TCC) boundaries, and combining tokens that are |
| parts of the same named-entity. |
| |
| :param str text: text to be tokenized into words |
| :param list taglist: a list of named entity tags to be used |
| :param class tagger: NER tagger engine |
| :return: list of words, tokenized from the text |
| """ |
| if not isinstance(text, str): |
| return [] |
|
|
| tagged_words = tagger.tag(text, pos=False) |
|
|
| words = [] |
| combining_word = "" |
| for idx, (curr_word, curr_tag) in enumerate(tagged_words): |
| if curr_tag != "O": |
| tag = curr_tag[2:] |
| else: |
| tag = "O" |
|
|
| if curr_tag.startswith("B-") and tag in taglist: |
| combining_word = curr_word |
| elif ( |
| curr_tag.startswith("I-") |
| and combining_word != "" |
| and tag in taglist |
| ): |
| combining_word += curr_word |
| elif curr_tag == "O" and combining_word != "": |
| words.append(combining_word) |
| combining_word = "" |
| words.append(curr_word) |
| else: |
| combining_word = "" |
| words.append(curr_word) |
| if idx + 1 == len(tagged_words): |
| if curr_tag.startswith("B-") and combining_word != "": |
| words.append(combining_word) |
| elif curr_tag.startswith("I-") and combining_word != "": |
| words.append(combining_word) |
| else: |
| pass |
|
|
| return words |
|
|
