Adaptive Word Sense Tagging on Chinese Corpus
Sue-jin Ker, Jen-Nan Chen · Institutional Repositories DataBase (IRDB) · 2004
This study describes a general framework for adaptive word sense disambiguation. The proposed framework begins with knowledge acquisition from the relatively easy context of a corpus. The proposed framework heavily relies on the adaptive step that enriches the initial knowledge base with knowledge gleaned from the partially disambiguated text. Once adjusted to fit the text at hand, the knowledge base is applied to the text again to finalize the disambiguation decision. The effectiveness of this approach was examined through sentences from the Sinica corpus. Experimental results indicated that adaptation significantly improved the performance of WSD. Moreover, the adaptive approach, achieved an applicability improvement from 33.0% up to 74.9% with a comparable precision.