Learning Chinese word embeddings from semantic and phonetic components

As an important task in Asian language information processing, Chinese word embedding learning has attracted much attention recently. Based on either Skip-gram or CBOW, several methods have been proposed to exploit Chinese characters and sub-character components for learning Chinese word embeddings. Chinese characters are combinations of meaning, structure, and phonetic information (pinyin). However, previous works only cover the former two aspects and cannot effectively explore distinct semantics of characters. To address this issue, we develop a Pinyin-enhance Skip-gram model named rsp2vec, in addition to a radical and pinyin-enhanced Chinese word embedding (rPCWE) learning models based on CBOW. For our models, the phonetic information and semantic components of Chinese characters are encoded into embeddings simultaneously. Evaluations on word analogy reasoning, word relevance, text classification, named entity recognition, and case studies validate the effectiveness of our models.