Chinese_keyBERT

Chinese_keyBERT is a minimal Chinese keywords extraction library that leverage the contextual embeddings generated from BERT models to extract relevant keywords from the given texts.

Installation

pip install chinese_keybert

Get started

from chinese_keybert import Chinese_Extractor
kw_extractor = Chinese_Extractor()
text = [
   '''
渾水創始人：七月開始調查貝殼，因為“好得難以置信” 2021年12月16日，做空機構渾水在社交媒體上公開表示，正在做空美股上市公司貝殼...
'''
]
result = kw_extractor.generate_keywords(text,top_k=5,rank_methods="mmr")

How it works

The core idea behind chinese_keyBERT is to utilize a word segmentation models to segments a piece of text into smaller n-grams and filter the n-grams according to the defined part-of-speech (as some pos are not suitable to be used as a keyword). Then, an embedding model (eg. BERT) is used to encode the text and filtered n_grams into embeddings and using some ranking methods (eg. maximun sum/maximun marginal relevance) to compute the cosine distances betweens the text and n-grams embeddings and rank the keywords according to the scores.

To-do

• Documentations
• Vectorization operations to speed-up processing of multiple documents
• Add support for other word segmentation, part-of-speech and embeddings model

Credit

Chinese_keyBERT was largely inspired by KeyBERT, a minimal library for embedding based keywords extractions. Besides, Chinese_keyBERT is also heavily relies on Chinese word segmentation and POS library from CKIP as well as sentence-transformer for generating quality embeddings.