Pre-trained Chinese word vectors

Basic Settings

                       

Window Size	Dynamic Window	Sub-sampling	Low-Frequency Word
5	Yes	1e-5	10

Various Domains

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

Word2vec / Skip-Gram with Negative Sampling (SGNS)
Corpus	Context Features
	Word	Word + Ngram	Word + Character	Word + Character + Ngram
Baidu Encyclopedia 百度百科	300d	300d	300d	300d
Wikipedia_zh 中文维基百科	300d	300d	300d	300d
People's Daily News 人民日报	300d	300d	300d	300d
Sogou News 搜狗新闻	300d	300d	300d	300d
Financial News 金融新闻	300d	300d	300d	300d
Zhihu_QA 知乎问答	300d	300d	300d	300d
Weibo 微博	300d	300d	300d	300d
Literature 文学作品	300d	300d	300d	300d
Complete Library in Four Sections 四库全书*	300d	300d	NAN	NAN
Mixed-large 综合	300d	300d	300d	300d

Positive Pointwise Mutual Information (PPMI)
Corpus	Context Features
	Word	Word + Ngram	Word + Character	Word + Character + Ngram
Baidu Encyclopedia 百度百科	300d	300d	300d	300d
Wikipedia_zh 中文维基百科	300d	300d	300d	300d
People's Daily News 人民日报	300d	300d	300d	300d
Sogou News 搜狗新闻	300d	300d	300d	300d
Financial News 金融新闻	300d	300d	300d	300d
Zhihu_QA 知乎问答	300d	300d	300d	300d
Weibo 微博	300d	300d	300d	300d
Literature 文学作品	300d	300d	300d	300d
Complete Library in Four Sections 四库全书*	300d	300d	NAN	NAN
Mixed-large 综合	300d	300d	300d	300d

^*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.

                                                       

Feature	Co-occurrence Type	Target Word Vectors	Context Word Vectors
Word	Word → Word	300d	300d
Ngram	Word → Ngram (1-2)	300d	300d
	Word → Ngram (1-3)	300d	300d
	Ngram (1-2) → Ngram (1-2)	300d	300d
Character	Word → Character (1)	300d	300d
	Word → Character (1-2)	300d	300d
	Word → Character (1-4)	300d	300d
Radical	Radical	300d	300d
Position	Word → Word (left/right)	300d	300d
	Word → Word (distance)	300d	300d
Global	Word → Text	300d	300d
Syntactic Feature	Word → POS	300d	300d
	Word → Dependency	300d	300d