Unsupervised Clustering of Morphologically Related Chinese Words

Chia Ling Lee, Ya Ning Chang, Chao Lin Liu, Chia Ying Lee, Jane Yung-Jen Hsu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Many linguists consider morphological awareness a major factor that affects children's reading development. A Chinese character embedded in different compound words may carry related but different meanings. For example, “商店(store)”, “商品(commodity)”, “商代(Shang Dynasty)”, and “商朝(Shang Dynasty)” can form two clusters: {“商店”, “商品”} and {“商代”, “商朝”}. In this paper, we aim at unsupervised clustering of a given family of morphologically related Chinese words. Successfully differentiating these words can contribute to both computer assisted Chinese learning and natural language understanding. In Experiment 1, we employed linguistic factors at the word, syntactic, semantic, and contextual levels in aggregated computational linguistics methods to handle the clustering task. In Experiment 2, we recruited adults and children to perform the clustering task. Experimental results indicate that our computational model achieved the same level of performance as children.

Original languageEnglish
Title of host publicationProceedings of the 36th Annual Meeting of the Cognitive Science Society, CogSci 2014
PublisherThe Cognitive Science Society
Pages2543-2548
Number of pages6
ISBN (Electronic)9780991196708
Publication statusPublished - 2014
Event36th Annual Meeting of the Cognitive Science Society, CogSci 2014 - Quebec City, Canada
Duration: 2014 Jul 232014 Jul 26

Publication series

NameProceedings of the 36th Annual Meeting of the Cognitive Science Society, CogSci 2014

Conference

Conference36th Annual Meeting of the Cognitive Science Society, CogSci 2014
Country/TerritoryCanada
CityQuebec City
Period14-07-2314-07-26

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computer Science Applications
  • Human-Computer Interaction
  • Cognitive Neuroscience

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