Proceedings of the Annual Meeting of the Cognitive Science Society

We propose an augmented word embedding model that better incorporates subword information with additional parametersthat characterize the semantic weights of characters in composing words. Our model can reveal some interesting patternsof long-term change in Chinese language, which provides novel evidence and methodology that enriches existing theoriesin evolutionary linguistics. The resulting word vectors also has decent performance in NLP-related tasks.