# -*- coding: utf-8 -*-
"""
Tool for create word list
code is 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 begining and ending index pairs of words
"""
i = 0
for w in words:
yield i, i + len(w)
i += len(w)
[docs]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 considered making `tokenize` perform unwell
: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
# if wrong more than right, then it's a bad word
bad_words = []
for w, count in wrong.items():
if count > right[w]:
bad_words.append(w)
return set(bad_words)
[docs]def revise_wordset(
tokenize: Callable[[str], List[str]],
orig_words: Iterable[str],
training_data: Iterable[Iterable[str]],
) -> Set[str]:
"""
Revise a set of word that could improve tokenization performance of
a dictionary-based `tokenize` function.
`orign_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 considered making `tokenize` perform unwell
: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
[docs]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 considered making `tokenize` perform unwell
: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