pythainlp.word_vector

The word_vector contains functions that makes use of a pre-trained vector public data. The pythainlp.word_vector module is a valuable resource for working with pre-trained word vectors. These word vectors are trained on large corpora and can be used for various natural language processing tasks, such as word similarity, document similarity, and more.

Dependencies

Installation of numpy and gensim is required.

Before using this module, you need to ensure that the numpy and gensim libraries are installed in your environment. These libraries are essential for loading and working with the pre-trained word vectors.

Modules

class pythainlp.word_vector.WordVector(model_name: str = 'thai2fit_wv')[source]

Provide Thai word vectors.

Parameters:

model_name (str) –

name of the word vector model

thai2fit_wv - word vectors from thai2fit (default)
ltw2v - word vectors from LTW2V: The Large Thai Word2Vec v0.1
ltw2v_v1.0_15_window - word vectors from LTW2V v1.0 with a window size of 15
ltw2v_v1.0_5_window - word vectors from LTW2V v1.0 with a window size of 5

The WordVector class encapsulates word vector operations and functions. It provides a convenient interface for loading models, finding word similarities, and generating sentence vectors.

__init__(model_name: str = 'thai2fit_wv') → None[source]

Initialize the word vector.

Parameters:: model_name (str) – name of the word vector model

load_wordvector(model_name: str) → None[source]

Load a word vector model.

Parameters:: model_name (str) – name of the word vector model
Raises:: FileNotFoundError – if the model corpus is not found

get_model() → Word2VecKeyedVectors[source]

Return the word vector model.

Returns:: gensim word2vec model
Return type:: gensim.models.keyedvectors.Word2VecKeyedVectors

doesnt_match(words: list[str]) → str[source]

Return the word that is the most unrelated to other words.

Use the function doesnt_match() from gensim.

Parameters:

words (list[str]) – list of words

Returns:

the most unrelated word

Return type:

str

Raises:

KeyError – if a word in words is not in the vocabulary of the model

Note:

If a word in words is not in the vocabulary, KeyError will be raised.

Example:

Pick the word “พริกไทย” (name of food) out of the list of meals (“อาหารเช้า”, “อาหารเที่ยง”, “อาหารเย็น”).

>>> from pythainlp.word_vector import WordVector
>>>
>>> wv = WordVector()
>>> words = ["อาหารเช้า", "อาหารเที่ยง", "อาหารเย็น", "พริกไทย"]
>>> wv.doesnt_match(words)
'พริกไทย'

Pick the word “เรือ” (name of vehicle) out of the list of words related to occupation (“ดีไซน์เนอร์”, “พนักงานเงินเดือน”, “หมอ”).

>>> from pythainlp.word_vector import WordVector
>>>
>>> wv = WordVector()
>>> words = ["ดีไซน์เนอร์", "พนักงานเงินเดือน", "หมอ", "เรือ"]
>>> wv.doesnt_match(words)
'เรือ'

most_similar_cosmul(positive: list[str], negative: list[str]) → list[tuple[str, float]][source]

Find the top-10 words most similar to two lists of words.

The two lists are labeled as positive and negative. The top-10 most similar words are obtained using the multiplication combination objective from Omer Levy and Yoav Goldberg [OmerLevy_YoavGoldberg_2014].

Use the function gensim.most_similar_cosmul() directly from gensim.

Parameters:

positive (list[str]) – list of words to add
negative (list[str]) – list of words to subtract

Returns:

list of the top-10 most similar words and their similarity scores

Return type:

list[tuple[str, float]]

Raises:

KeyError – if a word in positive or negative is not in the vocabulary of the model

Note:

With a single word in the positive list, it finds the most similar words to the given word (similar to gensim.most_similar())
If a word in positive or negative is not in the vocabulary, KeyError will be raised.

Example:

Find the top-10 most similar words to the word: “แม่น้ำ”.

>>> from pythainlp.word_vector import WordVector
>>>
>>> wv = WordVector()
>>> list_positive = ["แม่น้ำ"]
>>> list_negative = []
>>> wv.most_similar_cosmul(list_positive, list_negative)
[('ลำน้ำ', 0.8206598162651062), ('ทะเลสาบ', 0.775945782661438),
('ลุ่มน้ำ', 0.7490593194961548), ('คลอง', 0.7471904754638672),
('ปากแม่น้ำ', 0.7354257106781006), ('ฝั่งแม่น้ำ', 0.7120099067687988),
('ทะเล', 0.7030453681945801), ('ริมแม่น้ำ', 0.7015200257301331),
('แหล่งน้ำ', 0.6997432112693787), ('ภูเขา', 0.6960948705673218)]

Find the top-10 most similar words to the words: “นายก”, “รัฐมนตรี”, and “ประเทศ”.

>>> from pythainlp.word_vector import WordVector
>>>
>>> wv = WordVector()
>>> list_positive = ["นายก", "รัฐมนตรี", "ประเทศ"]
>>> list_negative = []
>>> wv.most_similar_cosmul(list_positive, list_negative)
[('รองนายกรัฐมนตรี', 0.2730445861816406),
('เอกอัครราชทูต', 0.26500266790390015),
('นายกรัฐมนตรี', 0.2649088203907013),
('ผู้ว่าราชการจังหวัด', 0.25119125843048096),
('ผู้ว่าการ', 0.2510434687137604), ('เลขาธิการ', 0.24824175238609314),
('ผู้ว่า', 0.2453523576259613), ('ประธานกรรมการ', 0.24147476255893707),
('รองประธาน', 0.24123257398605347), ('สมาชิกวุฒิสภา',
0.2405330240726471)]

Find the top-10 most similar words when having only positive list and both positive and negative lists.

>>> from pythainlp.word_vector import WordVector
>>>
>>> wv = WordVector()
>>> list_positive = ["ประเทศ", "ไทย", "จีน", "ญี่ปุ่น"]
>>> list_negative = []
>>> wv.most_similar_cosmul(list_positive, list_negative)
[('ประเทศจีน', 0.22022421658039093), ('เกาหลี', 0.2196873426437378),
('สหรัฐอเมริกา', 0.21660110354423523),
('ประเทศญี่ปุ่น', 0.21205860376358032),
('ประเทศไทย', 0.21159221231937408), ('เกาหลีใต้',
0.20321202278137207),
('อังกฤษ', 0.19610872864723206), ('ฮ่องกง', 0.1928885132074356),
('ฝรั่งเศส', 0.18383873999118805), ('พม่า', 0.18369348347187042)]
>>>
>>> list_positive = ["ประเทศ", "ไทย", "จีน", "ญี่ปุ่น"]
>>> list_negative = ["อเมริกา"]
>>> wv.most_similar_cosmul(list_positive, list_negative)
[('ประเทศไทย', 0.3278159201145172), ('เกาหลี', 0.3201899230480194),
('ประเทศจีน', 0.31755179166793823), ('พม่า', 0.30845439434051514),
('ประเทศญี่ปุ่น', 0.306713730096817),
('เกาหลีใต้', 0.3003999888896942),
('ลาว', 0.2995176911354065), ('คนไทย', 0.2885020673274994),
('เวียดนาม', 0.2878379821777344), ('ชาวไทย', 0.28480708599090576)]

The function raises KeyError when the term “เมนูอาหารไทย” is not in the vocabulary.

>>> from pythainlp.word_vector import WordVector
>>>
>>> wv = WordVector()
>>> list_positive = ["เมนูอาหารไทย"]
>>> list_negative = []
>>> wv.most_similar_cosmul(list_positive, list_negative)
KeyError: "word 'เมนูอาหารไทย' not in vocabulary"

similarity(word1: str, word2: str) → float[source]

Compute the cosine similarity between two words.

Parameters:

word1 (str) – first word to be compared
word2 (str) – second word to be compared

Returns:

cosine similarity between the two word vectors

Return type:

float

Raises:

KeyError – if word1 or word2 is not in the vocabulary of the model

Note:

If a word in word1 or word2 is not in the vocabulary, KeyError will be raised.

Example:

Compute cosine similarity between two words: “รถไฟ” and “รถไฟฟ้า” (train and electric train).

>>> from pythainlp.word_vector import WordVector
>>> wv = WordVector()
>>> wv.similarity("รถไฟ", "รถไฟฟ้า")
0.43387136

Compute cosine similarity between two words: “เสือดาว” and “รถไฟฟ้า” (leopard and electric train).

>>> from pythainlp.word_vector import WordVector
>>>
>>> wv = WordVector()
>>> wv.similarity("เสือดาว", "รถไฟฟ้า")
0.04300258

sentence_vectorizer(text: str, use_mean: bool = True) → NDArray[np.float32][source]

Convert Thai text into a vector.

First tokenize the text and map each word to its word vector from the model. Then aggregate the word vectors into one vector of 300 dimensions, by the mean or the sum of all word vectors.

Parameters:

text (str) – text to be vectorized
use_mean (bool) – if True, aggregate word vectors by their mean; otherwise, aggregate by their sum

Returns:

numpy.ndarray of dtype numpy.float32 and shape (1, 300) representing the given text

Return type:

numpy.typing.NDArray[numpy.float32]

Example:

Vectorize the sentence, “อ้วนเสี้ยวเข้ายึดแคว้นกิจิ๋ว ในปี พ.ศ. 735”, into one sentence vector with two aggregation methods: mean and summation.

>>> from pythainlp.word_vector import WordVector
>>>
>>> wv = WordVector()
>>> sentence = "อ้วนเสี้ยวเข้ายึดแคว้นกิจิ๋ว ในปี พ.ศ. 735"
>>> wv.sentence_vectorizer(sentence, use_mean=True)
array([[-0.00421414, -0.08881307,  0.05081136, -0.05632929,
     -0.06607185, 0.03059357, -0.113882  , -0.00074836,  0.05035743,
     0.02914307,
     ...
    0.02893357,  0.11327957,  0.04562086, -0.05015393,  0.11641257,
    0.32304936, -0.05054322,  0.03639471, -0.06531371,  0.05048079]])
>>>
>>> wv.sentence_vectorizer(sentence, use_mean=False)
array([[-0.05899798, -1.24338295,  0.711359  , -0.78861002,
     -0.92500597, 0.42831   , -1.59434797, -0.01047703,  0.705004
    ,  0.40800299,
    ...
    0.40506999,  1.58591403,  0.63869202, -0.702155  ,  1.62977601,
    4.52269109, -0.70760502,  0.50952601, -0.914392  ,  0.70673105]])

References

[Omer Levy and Yoav Goldberg (2014). Linguistic Regularities in Sparse and Explicit Word Representations](https://www.aclweb.org/anthology/W14-1618/) This reference points to the work by Omer Levy and Yoav Goldberg, which discusses linguistic regularities in word representations. It underlines the theoretical foundation of word vectors and their applications in NLP.

This enhanced documentation provides a more detailed and organized overview of the pythainlp.word_vector module, making it a valuable resource for NLP practitioners and researchers working with pre-trained word vectors in the Thai language.