Modeling of semantic space is a new and challenging research topic both in cognitive science and linguistics. Existing approaches can be classified into two different types according to how the calculation are done: either a word-by-word co-occurrence matrix or a word-by-context matrix (Riordan 2007). In this paper, we argue that the existing popular distributional semantic model (vector space model), does not adequately explain the age-ofacquisition data in Chinese. An alternatively measure of semantic proximity called PROX (Gaume et al, 2006) is applied instead. The application or PROX has interesting psycholinguistic implications. Unlike previous semantic space models, PROX can be trained with children's data as well as adult data. This allows us to test the hypothesis that children's semantic space approximates the target of acquisition: adult's semantic space. It also allows us to compare our Chinese experiment results with French results to see to attest the universality of the approximation model. Copyright © 2009 by Shu-Kai Hsieh, Chun-Han Chang, Ivy Kuo, Hintat Cheung, Chu-Ren Huang, and Bruno Gaume.
|Title of host publication||PACLIC 23: Proceedings of the 23rd Pacific Asia Conference on Language, Information and Computation|
|Place of Publication||Hong Kong|
|Publisher||City University of Hong Kong Press|
|ISBN (Print)||9789624423204, 9624423202|
|Publication status||Published - 2009|