Chinese Word Vectors 中文词向量

中文

This project provides 100+ Chinese Word Vectors (embeddings) trained with different representations (dense and sparse), context features (word, ngram, character, and more), and corpora. One can easily obtain pre-trained vectors with different properties and use them for downstream tasks.

Moreover, we provide a Chinese analogical reasoning dataset CA8 and an evaluation toolkit for users to evaluate the quality of their word vectors.

Reference

Please cite the paper, if using these embeddings and CA8 dataset.

Shen Li, Zhe Zhao, Renfen Hu, Wensi Li, Tao Liu, Xiaoyong Du, Analogical Reasoning on Chinese Morphological and Semantic Relations, ACL 2018.

@InProceedings{P18-2023,
  author =  "Li, Shen
    and Zhao, Zhe
    and Hu, Renfen
    and Li, Wensi
    and Liu, Tao
    and Du, Xiaoyong",
  title =   "Analogical Reasoning on Chinese Morphological and Semantic Relations",
  booktitle =   "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
  year =  "2018",
  publisher =   "Association for Computational Linguistics",
  pages =   "138--143",
  location =  "Melbourne, Australia",
  url =   "http://aclweb.org/anthology/P18-2023"
}

A detailed analysis of the relation between the intrinsic and extrinsic evaluations of Chinese word embeddings is shown in the paper:

Yuanyuan Qiu, Hongzheng Li, Shen Li, Yingdi Jiang, Renfen Hu, Lijiao Yang. Revisiting Correlations between Intrinsic and Extrinsic Evaluations of Word Embeddings. Chinese Computational Linguistics and Natural Language Processing Based on Naturally Annotated Big Data. Springer, Cham, 2018. 209-221. (CCL & NLP-NABD 2018 Best Paper)

@incollection{qiu2018revisiting,
  title={Revisiting Correlations between Intrinsic and Extrinsic Evaluations of Word Embeddings},
  author={Qiu, Yuanyuan and Li, Hongzheng and Li, Shen and Jiang, Yingdi and Hu, Renfen and Yang, Lijiao},
  booktitle={Chinese Computational Linguistics and Natural Language Processing Based on Naturally Annotated Big Data},
  pages={209--221},
  year={2018},
  publisher={Springer}
}

Format

The pre-trained vector files are in text format. Each line contains a word and its vector. Each value is separated by space. The first line records the meta information: the first number indicates the number of words in the file and the second indicates the dimension size.

Besides dense word vectors (trained with SGNS), we also provide sparse vectors (trained with PPMI). They are in the same format with liblinear, where the number before " : " denotes dimension index and the number after the " : " denotes the value.

Pre-trained Chinese Word Vectors

Basic Settings

^*Only for SGNS.

Various Domains

Chinese Word Vectors trained with different representations, context features, and corpora.