Chinese word embeddings have recently garnered considerable attention. Chinese characters and their sub-character components, which contain rich semantic information, are incorporated to learn Chinese word embeddings. Chinese characters can represent a combination of meaning, structure, and pronunciation. However, existing embedding learning methods focus on the structure and meaning of Chinese characters. In this study, we aim to develop an embedding learning method that can make complete use of the information represented by Chinese characters, including phonology, morphology, and semantics. Specifically, we propose a pronunciation-enhanced Chinese word embedding learning method, where the pronunciations of context characters and target characters are simultaneously encoded into the embeddings. Evaluation of word similarity, word analogy reasoning, text classification, and sentiment analysis validate the effectiveness of our proposed method.

中文单词嵌入最近引起了相当大的关注。汉字及其子字符组件（包含丰富的语义信息）被合并用于学习汉字嵌入。汉字可以表示含义，结构和发音的组合。但是，现有的嵌入学习方法集中于汉字的结构和含义。在这项研究中，我们旨在开发一种嵌入学习方法，该方法可以充分利用汉字表示的信息，包括语音，形态和语义。具体来说，我们提出了一种语音增强的中文单词嵌入学习方法，该方法将上下文字符和目标字符的发音同时编码到嵌入中。单词相似度评估