TitleEnsemble of RFR_SUM unigram and bigram for Chinese WSD
AuthorsQu, Weiguang
Yu, Jingsong
Zhou, Junsheng
Shao, Yanqiu
Li, Sujian
Sui, Zhifang
AffiliationInstitute of Computational Linguistics, Peking Univ., Beijing 100871, China
Department of Computer Science, Nanjing Normal Univ., Nanjing 210097, China
School of Software and Microelectronics, Peking Univ., Beijing 102600, China
Issue Date2007
Publisherjournal of computational information systems
CitationJournal of Computational Information Systems.2007,3,(5),1867-1874.
AbstractIn this paper, we expand a collocation-based WSD model RFR-SUM (sum of Relative Frequency Ratio in context) from unigram (UNIRFRSUM) to bigram (BIRFRSUM) and design two algorithms for BI_RFR_SUM: Simple BI_RFR_SUM algorithm (SBI) and No Intersection BI_RFR_SUM algorithm (NI). We select 7 frequently used polysemous words as examples and the experiments show that the precision of NI algorithm can be adjusted to a very high level. We combine UNI_RFR_SUM with NI algorithm and get a precision of 96.40% with respect to that of TJNI_RFR_SUM 93-23% and SBI 93.32% in open test. This means that the ensemble learning can reduce 46.82% misclassifieation of UNIJRFRSUM model.
URIhttp://hdl.handle.net/20.500.11897/407574
ISSN15539105
IndexedEI
Appears in Collections:软件与微电子学院
计算语言学教育部重点实验室

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