Source codes of our EMNLP2017 paper Joint Embeddings of Chinese Words, Characters, and Fine-grained Subcharacter Components

You need to prepare a training corpus and the Chinese subcharacter radicals or components.

• Training corpus. Download Chinese Wikipedia Dump. Following the instractions on the blog, you can extract the raw content from the xml file and do data preprocessing such as removing pure digits and non Chinese characters. Alternatively, you can download the corpus after preprocessing at the onlibe baidu box.
• Subcharacter radicals and components. Deploy the scrapy codes in JWE/ChineseCharCrawler on Scrapy Cloud, you can crawl the resource from HTTPCN. We provide a copy of the data in ./subcharacters for reserach convenience. The copyright and all rights therein of the subcharacter data are reserved by the website HTTPCN.

• cd JWE/src, compile the code by make all.
• run ./jwe for parameters details.
• run ./run.sh to start the model training, you may modify the parameters in file run.sh.
• Input files format: Corpus wiki.txt contains segmented Chinese words with UTF-8 encoding; Subcharacters comp.txt contains a list of components which are seperated by blank spaces; char2comp.txt, each line consists of a Chinese character and its components in the following format:

侩 亻 人 云
侨 亻 乔
侧 亻 贝 刂
侦 亻 卜 贝

Two Chinese word similarity datasets 240.txt and 297.txt and one Chinese analogy dataset analogy.txt in JWE/evaluation folder are provided by (Chen et al., IJCAI, 2015).

cd JWE/src, then

• run python word_sim.py -s <similarity_file> -e <embed_file> for word similarity evaluation, where similarity_file is the word similarity file, e.g., 240.txt or 297.txt, embed_file is the trained word embedding file.
• run python word_analogy.py -a <analogy_file> -e <embed_file> or ./word_analogy <embed_file> <analogy_file> for word analogy evaluation.