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Query-by-semantic-description (QBSD) is a natural way for searching/annotating music in a large database. To improve QBSD, we propose the use of anti-words for each annotation word based on the concept of supervised multiclass labeling (SML). More specifically, words that are highly associated with the opposite semantic meaning of a word constitute its anti-word set. By modeling both a word and its anti-word set, our annotation system can achieve 31.1% of equal mean per-word precision and recall, while the original SML model achieves 27.8%. Moreover, by constructing the models of the anti-word explicitly, the performance is also significantly improved for the retrieval system, especially when the query keyword is the antonym of an existing annotation word.
Audio, Speech, and Language Processing, IEEE Transactions on (Volume:17 , Issue: 8 )
Date of Publication: Nov. 2009