Chinese Word Vectors 中文词向量

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

Window SizeDynamic WindowSub-samplingLow-Frequency WordIterationNegative Sampling*
5Yes1e-51055

*Only for SGNS.

Various Domains

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

Word2vec / Skip-Gram with Negative Sampling (SGNS)
CorpusContext Features
WordWord + NgramWord + CharacterWord + Character + Ngram
Baidu Encyclopedia 百度百科300d300d300d300d / PWD: 5555
Wikipedia_zh 中文维基百科300d300d300d300d
People's Daily News 人民日报300d300d300d300d
Sogou News 搜狗新闻300d300d300d300d
Financial News 金融新闻300d300d300d300d
Zhihu_QA 知乎问答300d300d300d300d
Weibo 微博300d300d300d300d
Literature 文学作品300d300d / PWD: z5b4300d300d / PWD: yenb
Complete Library in Four Sections
四库全书*
300d300dNANNAN
Mixed-large 综合
Baidu Netdisk / Google Drive
300d
300d
300d
300d
300d
300d
300d
300d
Positive Pointwise Mutual Information (PPMI)
CorpusContext Features
WordWord + NgramWord + CharacterWord + Character + Ngram
Baidu Encyclopedia 百度百科SparseSparseSparseSparse
Wikipedia_zh 中文维基百科SparseSparseSparseSparse
People's Daily News 人民日报SparseSparseSparseSparse
Sogou News 搜狗新闻SparseSparseSparseSparse
Financial News 金融新闻SparseSparseSparseSparse
Zhihu_QA 知乎问答SparseSparseSparseSparse
Weibo 微博SparseSparseSparseSparse
Literature 文学作品SparseSparseSparseSparse
Complete Library in Four Sections
四库全书*
SparseSparseNANNAN
Mixed-large 综合SparseSparseSparseSparse

*Character embeddings are provided, since most of Hanzi are words in the archaic Chinese.

Various Co-occurrence Information

We release word vectors upon different co-occurrence statistics. Target and context vectors are often called input and output vectors in some related papers.

In this part, one can obtain vectors of arbitrary linguistic units beyond word. For example, character vectors is in the context vectors of word-character.

All vectors are trained by SGNS on Baidu Encyclopedia.

FeatureCo-occurrence TypeTarget Word VectorsContext Word Vectors
WordWord → Word300d300d
NgramWord → Ngram (1-2)300d300d
Word → Ngram (1-3)300d300d
Ngram (1-2) → Ngram (1-2)300d300d
CharacterWord → Character (1)300d300d
Word → Character (1-2)300d300d
Word → Character (1-4)300d300d
RadicalRadical300d300d
PositionWord → Word (left/right)300d300d
Word → Word (distance)300d300d
GlobalWord → Text300d300d
Syntactic FeatureWord → POS300d300d
Word → Dependency300d300d

Representations

Existing word representation methods fall into one of the two classes, dense and sparse represnetations. SGNS model (a model in word2vec toolkit) and PPMI model are respectively typical methods of these two classes. SGNS model trains low-dimensional real (dense) vectors through a shallow neural network. It is also called neural embedding method. PPMI model is a sparse bag-of-feature representation weighted by positive-pointwise-mutual-information (PPMI) weighting scheme.

Context Features

Three context features: word, ngram, and character are commonly used in the word embedding literature. Most word representation methods essentially exploit word-word co-occurrence statistics, namely using word as context feature (word feature). Inspired by language modeling problem, we introduce ngram feature into the context. Both word-word and word-ngram co-occurrence statistics are used for training (ngram feature). For Chinese, characters (Hanzi) often convey strong semantics. To this end, we consider using word-word and word-character co-occurrence statistics for learning word vectors. The length of character-level ngrams ranges from 1 to 4 (character feature).

Besides word, ngram, and character, there are other features which have substantial influence on properties of word vectors. For example, using entire text as context feature could introduce more topic information into word vectors; using dependency parse as context feature could add syntactic constraint to word vectors. 17 co-occurrence types are considered in this project.

Corpus

We made great efforts to collect corpus across various domains. All text data are preprocessed by removing html and xml tags. Only the plain text are kept and HanLP(v_1.5.3) is used for word segmentation. In addition, traditional Chinese characters are converted into simplified characters with Open Chinese Convert (OpenCC). The detailed corpora information is listed as follows:

CorpusSizeTokensVocabulary SizeDescription
Baidu Encyclopedia
百度百科
4.1G745M5422KChinese Encyclopedia data from
https://baike.baidu.com/
Wikipedia_zh
中文维基百科
1.3G223M2129KChinese Wikipedia data from
https://dumps.wikimedia.org/
People's Daily News
人民日报
3.9G668M1664KNews data from People's Daily(1946-2017)
http://data.people.com.cn/
Sogou News
搜狗新闻
3.7G649M1226KNews data provided by Sogou labs
http://www.sogou.com/labs/
Financial News
金融新闻
6.2G1055M2785KFinancial news collected from multiple news websites
Zhihu_QA
知乎问答
2.1G384M1117KChinese QA data from
https://www.zhihu.com/
Weibo
微博
0.73G136M850KChinese microblog data provided by NLPIR Lab
http://www.nlpir.org/wordpress/download/weibo.7z
Literature
文学作品
0.93G177M702K8599 modern Chinese literature works
Mixed-large
综合
22.6G4037M10653KWe build the large corpus by merging the above corpora.
Complete Library in Four Sections
四库全书
1.5G714M21.8KThe largest collection of texts in pre-modern China.

All words are concerned, including low frequency words.

Toolkits

All word vectors are trained by ngram2vec toolkit. Ngram2vec toolkit is a superset of word2vec and fasttext toolkit, where arbitrary context features and models are supported.

Chinese Word Analogy Benchmarks

The quality of word vectors is often evaluated by analogy question tasks. In this project, two benchmarks are exploited for evaluation. The first is CA-translated, where most analogy questions are directly translated from English benchmark. Although CA-translated has been widely used in many Chinese word embedding papers, it only contains questions of three semantic questions and covers 134 Chinese words. In contrast, CA8 is specifically designed for Chinese language. It contains 17813 analogy questions and covers comprehensive morphological and semantic relations. The CA-translated, CA8, and their detailed descriptions are provided in testsets folder.

Evaluation Toolkit

We present an evaluation toolkit in evaluation folder.

Run the following codes to evaluate dense vectors.

$ python ana_eval_dense.py -v <vector.txt> -a CA8/morphological.txt
$ python ana_eval_dense.py -v <vector.txt> -a CA8/semantic.txt

Run the following codes to evaluate sparse vectors.

$ python ana_eval_sparse.py -v <vector.txt> -a CA8/morphological.txt
$ python ana_eval_sparse.py -v <vector.txt> -a CA8/semantic.txt
GitHub - Embedding/Chinese-Word-Vectors at pythonawesome.com
100+ Chinese Word Vectors 上百种预训练中文词向量 . Contribute to Embedding/Chinese-Word-Vectors development by creating an account on GitHub.