Word Space Modeling for Measuring Semantic Specificity in Chinese

The aim of this study is to use the word-space model to measure the semantic loads of single verbs, profile verbal lexicon acquisition, and explore the semantic information on Chinese resultative verb compounds (RVCs). A distributional model based on Academia Sinica Balanced Corpus (ASBC) with Latent Semantic Analysis (LSA) is built to investigate the semantic space variation depending on the semantic loads/specificity. The between group comparison of age-related changes in verb style is then conducted to suggest the influence of semantic space on verbal acquisition. Finally, it demonstrates how meaning exploring on RVCs is done with semantic space.

• Type: Journal
• Year: 2010
• Where: COLING
• Authors: Ching-Fen Pan, Shu-Kai Hsieh
• Keywords: COLING 2010, Computational Linguistics, Semantic, Semantic Space, Semantic Space Variation