| |
| |
| |
| |
| """ |
| Tool for creating word lists |
| codes are from Korakot Chaovavanich. |
| |
| :See also: |
| * `Facebook post \ |
| <https://www.facebook.com/groups/colab.thailand/permalink/1667821073393244>`_ |
| * `Google Colab \ |
| <https://colab.research.google.com/drive/19kY2jCHONuxmTJM0U8PIE_I5OK1rO-x_>`_ |
| """ |
|
|
| from collections import Counter |
| from typing import Callable, Iterable, Iterator, List, Set, Tuple |
|
|
| from pythainlp.corpus import thai_words |
| from pythainlp.tokenize import newmm |
| from pythainlp.util import Trie |
|
|
|
|
| def index_pairs(words: List[str]) -> Iterator[Tuple[int, int]]: |
| """ |
| Return beginning and ending indexes of word pairs |
| """ |
| i = 0 |
| for w in words: |
| yield i, i + len(w) |
| i += len(w) |
|
|
|
|
| def find_badwords( |
| tokenize: Callable[[str], List[str]], |
| training_data: Iterable[Iterable[str]], |
| ) -> Set[str]: |
| """ |
| Find words that do not work well with the `tokenize` function |
| for the provided `training_data`. |
| |
| :param Callable[[str], List[str]] tokenize: a tokenize function |
| :param Iterable[Iterable[str]] training_data: tokenized text, to be used\ |
| as a training set |
| :return: words that are considered to make `tokenize` perform badly |
| :rtype: Set[str] |
| """ |
| right = Counter() |
| wrong = Counter() |
|
|
| for train_words in training_data: |
| train_set = set(index_pairs(train_words)) |
| test_words = tokenize("".join(train_words)) |
| test_pairs = index_pairs(test_words) |
| for w, p in zip(test_words, test_pairs): |
| if p in train_set: |
| right[w] += 1 |
| else: |
| wrong[w] += 1 |
|
|
| |
| bad_words = [] |
| for w, count in wrong.items(): |
| if count > right[w]: |
| bad_words.append(w) |
|
|
| return set(bad_words) |
|
|
|
|
| def revise_wordset( |
| tokenize: Callable[[str], List[str]], |
| orig_words: Iterable[str], |
| training_data: Iterable[Iterable[str]], |
| ) -> Set[str]: |
| """ |
| Revise a set of words that could improve tokenization performance of |
| a dictionary-based `tokenize` function. |
| |
| `orig_words` will be used as a base set for the dictionary. |
| Words that do not performed well with `training_data` will be removed. |
| The remaining words will be returned. |
| |
| :param Callable[[str], List[str]] tokenize: a tokenize function, can be\ |
| any function that takes a string as input and returns a List[str] |
| :param Iterable[str] orig_words: words that used by the tokenize function,\ |
| will be used as a base for revision |
| :param Iterable[Iterable[str]] training_data: tokenized text, to be used\ |
| as a training set |
| :return: words that are considered to make `tokenize` perform badly |
| :rtype: Set[str] |
| |
| :Example:: |
| :: |
| |
| from pythainlp.corpus import thai_words |
| from pythainlp.corpus.util import revise_wordset |
| from pythainlp.tokenize.longest import segment |
| |
| base_words = thai_words() |
| more_words = { |
| "ถวิล อุดล", "ทองอินทร์ ภูริพัฒน์", "เตียง ศิริขันธ์", "จำลอง ดาวเรือง" |
| } |
| base_words = base_words.union(more_words) |
| dict_trie = Trie(wordlist) |
| |
| tokenize = lambda text: segment(text, dict_trie) |
| |
| training_data = [ |
| [str, str, str. ...], |
| [str, str, str, str, ...], |
| ... |
| ] |
| |
| revised_words = revise_wordset(tokenize, wordlist, training_data) |
| """ |
| bad_words = find_badwords(tokenize, training_data) |
| return set(orig_words) - bad_words |
|
|
|
|
| def revise_newmm_default_wordset( |
| training_data: Iterable[Iterable[str]], |
| ) -> Set[str]: |
| """ |
| Revise a set of word that could improve tokenization performance of |
| `pythainlp.tokenize.newmm`, a dictionary-based tokenizer and a default |
| tokenizer for PyThaiNLP. |
| |
| Words from `pythainlp.corpus.thai_words()` will be used as a base set |
| for the dictionary. Words that do not performed well with `training_data` |
| will be removed. The remaining words will be returned. |
| |
| :param Iterable[Iterable[str]] training_data: tokenized text, to be used\ |
| as a training set |
| :return: words that are considered to make `tokenize` perform badly |
| :rtype: Set[str] |
| """ |
| orig_words = thai_words() |
| trie = Trie(orig_words) |
|
|
| def tokenize(text): |
| return newmm.segment(text, custom_dict=trie) |
|
|
| revised_words = revise_wordset(tokenize, orig_words, training_data) |
| return revised_words |
|
|
