Exploring Semantic Change of Chinese Word Using Crawled Web Data

摘要

Words changing their meanings over time reflects various shifts in socio-cultural attitudes and conceptual structures. Understanding semantic change of words over time is important in order to study models of language and cultural evolution. Word embeddings methods such as PPMI, SVD and word2vec have been evaluated in recent years. These kinds of representation methods, sometimes referring as semantic maps of words, are able to facilitate the whole process of language processing. Chinese language is no exception. The development of technology gradually influences people's communication and the language they are using. In the paper, a huge amount of data (300 GB) is provided by Sogou, a Chinese web search engine provider. After pre-processing, the Chinese language corpus is obtained. Three different word representation methods are extended to including temporal information. They are trained and tested based on the above dataset. A thorough analysis (both qualitative and quantitative analysis) is conducted with different thresholds to capture different semantic accuracy and alignment quality of the shifted words. A comparison between three methods is provided and possible reasons behind experiment results are discussed.