Identifying Different Meanings of a Chinese Morpheme through Latent Semantic Analysis and Minimum Spanning Tree Analysis

A character corresponds roughly to a morpheme in Chinese, and it usually takes on multiple meanings. In this paper, we aimed at capturing the multiple meanings of a Chinese morpheme across polymorphemic words in a growing semantic micro-space. Using Latent Semantic Analysis (LSA), we created several nested LSA semantic micro-spaces of increasing size. The term-document matrix of the smallest semantic space was obtained through filtering a whole corpus with a list of 192 Chinese polymorphemic words sharing a common morpheme ( gong1). For each of our created Chinese LSA space, we computed the whole cosine matrix of all the terms of the semantic space to measure semantic similarity between words. From the cosine matrix, we derived a dissimilarity matrix. This dissimilarity matrix was viewed as the adjacency matrix of a complete weighted undirected graph. We built from this graph a minimum spanning tree (MST). So, each of our LSA semantic space had its associated MST. It is shown that in our biggest MST, paths can be used to infer and capture the correct meaning of a morpheme embedded in a polymorphemic word. Clusters of the different meanings of a polysemous morpheme can be created from the minimum spanning tree. Finally, it is concluded that our approach could model partly human knowledge representation and acquisition of the different meanings of Chinese polysemous morphemes. Our work is thought to bring some insights to the Plato’s problem and additional evidence towards the plausibility of words serving as ungrounded symbols. Future directions are sketched. IJCLA VOL. 1, NO. 1-2, JAN-DEC 2010, PP. 153-168 RECEIVED 24/11/09 ACCEPTED 16/01/10 FINAL 11/03/10