File size: 5,038 Bytes
e4b9a7b | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 | # -*- coding: utf-8 -*-
# SPDX-FileCopyrightText: 2016-2025 PyThaiNLP Project
# SPDX-FileType: SOURCE
# SPDX-License-Identifier: Apache-2.0
from typing import List, Tuple, Union
from pythainlp.corpus import thai_wsd_dict
from pythainlp.tokenize import Tokenizer
from pythainlp.util.trie import Trie
_wsd_dict = thai_wsd_dict()
_mean_all = {}
for i, j in zip(_wsd_dict["word"], _wsd_dict["meaning"]):
_mean_all[i] = j
_all_word = set(list(_mean_all.keys()))
_TRIE = Trie(list(_all_word))
_word_cut = Tokenizer(custom_dict=_TRIE)
_MODEL = None
class _SentenceTransformersModel:
def __init__(
self,
model: str = "sentence-transformers/paraphrase-multilingual-mpnet-base-v2",
device: str = "cpu",
):
from sentence_transformers import SentenceTransformer
self.device = device
self.model_name = model
self.model = SentenceTransformer(self.model_name, device=self.device)
def change_device(self, device: str):
from sentence_transformers import SentenceTransformer
self.device = device
self.model = SentenceTransformer(self.model_name, device=self.device)
def get_score(self, sentences1: str, sentences2: str) -> float:
from sentence_transformers import util
embedding_1 = self.model.encode(sentences1, convert_to_tensor=True)
embedding_2 = self.model.encode(sentences2, convert_to_tensor=True)
return 1 - util.pytorch_cos_sim(embedding_1, embedding_2)[0][0].item()
def get_sense(
sentence: str,
word: str,
device: str = "cpu",
custom_dict: dict = dict(),
custom_tokenizer: Tokenizer = _word_cut,
) -> List[Tuple[str, float]]:
"""
Get word sense from the sentence.
This function will get definition and distance from context in sentence.
:param str sentence: Thai sentence
:param str word: Thai word
:param str device: device for running model on.
:param dict custom_dict: Thai dictionary {"word":["definition",..]}
:param Tokenizer custom_tokenizer: Tokenizer used to tokenize words in \
sentence.
:return: a list of definitions and distances (1 - cos_sim) or \
an empty list (if word is not in the dictionary)
:rtype: List[Tuple[str, float]]
We get the ideas from `Context-Aware Semantic Similarity Measurement for \
Unsupervised Word Sense Disambiguation \
<https://arxiv.org/abs/2305.03520>`_ to build get_sense function.
Use Thai dictionary from wiktionary.
See `thai_dict <https://pythainlp.org/pythainlp-corpus/thai_dict.html>`_.
Use sentence transformers model from \
`sentence-transformers/paraphrase-multilingual-mpnet-base-v2 \
<https://huggingface.co/sentence-transformers/paraphrase-multilingual-mpnet-base-v2>`_ \
for unsupervised word sense disambiguation.
:Example:
::
from pythainlp.wsd import get_sense
print(get_sense("เขากำลังอบขนมคุกกี้","คุกกี้"))
# output:
# [('โปรแกรมคอมพิวเตอร์ใช้ในทางอินเทอร์เน็ตสำหรับเก็บข้อมูลของผู้ใช้งาน',
# 0.0974416732788086),
# ('ชื่อขนมชนิดหนึ่งจำพวกขนมเค้ก แต่ทำเป็นชิ้นเล็ก ๆ แบน ๆ แล้วอบให้กรอบ',
# 0.09319090843200684)]
print(get_sense("เว็บนี้ต้องการคุกกี้ในการทำงาน","คุกกี้"))
# output:
# [('โปรแกรมคอมพิวเตอร์ใช้ในทางอินเทอร์เน็ตสำหรับเก็บข้อมูลของผู้ใช้งาน',
# 0.1005704402923584),
# ('ชื่อขนมชนิดหนึ่งจำพวกขนมเค้ก แต่ทำเป็นชิ้นเล็ก ๆ แบน ๆ แล้วอบให้กรอบ',
# 0.12473666667938232)]
"""
global _MODEL
if not custom_dict:
custom_dict = _mean_all
w = custom_tokenizer.word_tokenize(sentence)
if word not in set(custom_dict.keys()) or word not in sentence:
return []
if not _MODEL:
_MODEL = _SentenceTransformersModel(device=device)
if _MODEL.device != device:
_MODEL.change_device(device=device)
temp_mean = custom_dict[word]
temp = []
for i in temp_mean:
_temp_2 = []
for j in w:
if j == word:
j = (
word
+ f" ({word} ความหมาย '"
+ i.replace("(", "").replace(")", "")
+ "') "
)
_temp_2.append(j)
temp.append((i, _MODEL.get_score(sentence, "".join(_temp_2))))
return temp
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