TY - JOUR
T1 - Unsupervised WSD by finding the predominant sense using context as a dynamic thesaurus
AU - Tejada-Cárcamo, Javier
AU - Calvo, Hiram
AU - Gelbukh, Alexander
AU - Hara, Kazuo
N1 - Funding Information:
SMUaw CNTS-Antwerp Sinequa-LIA - HMM MFS UNED-AW-U2 UNED-AW-U UCLA-gchao2 UCLA-gchao3 CL Research-DIMAP CL Research-DIMAP (R) UCLA-gchao Regular Paper Supported by the Mexican Government (SNI, SIP-IPN, COFAA-IPN, and PIFI-IPN), CONACYT and the Japanese Government. ©2010 Springer Science + Business Media, LLC & Science Press, China
PY - 2010/9
Y1 - 2010/9
N2 - We present and analyze an unsupervised method for Word Sense Disambiguation (WSD). Our work is based on the method presented by McCarthy et al. in 2004 for finding the predominant sense of each word in the entire corpus. Their maximization algorithm allows weighted terms (similar words) from a distributional thesaurus to accumulate a score for each ambiguous word sense, i.e., the sense with the highest score is chosen based on votes from a weighted list of terms related to the ambiguous word. This list is obtained using the distributional similarity method proposed by Lin Dekang to obtain a thesaurus. In the method of McCarthy et al., every occurrence of the ambiguous word uses the same thesaurus, regardless of the context where the ambiguous word occurs. Our method accounts for the context of a word when determining the sense of an ambiguous word by building the list of distributed similar words based on the syntactic context of the ambiguous word. We obtain a top precision of 77.54% of accuracy versus 67.10% of the original method tested on SemCor. We also analyze the effect of the number of weighted terms in the tasks of finding the Most Frecuent Sense (MFS) and WSD, and experiment with several corpora for building the Word Space Model.
AB - We present and analyze an unsupervised method for Word Sense Disambiguation (WSD). Our work is based on the method presented by McCarthy et al. in 2004 for finding the predominant sense of each word in the entire corpus. Their maximization algorithm allows weighted terms (similar words) from a distributional thesaurus to accumulate a score for each ambiguous word sense, i.e., the sense with the highest score is chosen based on votes from a weighted list of terms related to the ambiguous word. This list is obtained using the distributional similarity method proposed by Lin Dekang to obtain a thesaurus. In the method of McCarthy et al., every occurrence of the ambiguous word uses the same thesaurus, regardless of the context where the ambiguous word occurs. Our method accounts for the context of a word when determining the sense of an ambiguous word by building the list of distributed similar words based on the syntactic context of the ambiguous word. We obtain a top precision of 77.54% of accuracy versus 67.10% of the original method tested on SemCor. We also analyze the effect of the number of weighted terms in the tasks of finding the Most Frecuent Sense (MFS) and WSD, and experiment with several corpora for building the Word Space Model.
KW - Semantic similarity
KW - Text corpus
KW - Thesaurus
KW - Word sense disambiguation
KW - Word space model
UR - http://www.scopus.com/inward/record.url?scp=78650204798&partnerID=8YFLogxK
U2 - 10.1007/s11390-010-9385-2
DO - 10.1007/s11390-010-9385-2
M3 - Artículo
SN - 1000-9000
VL - 25
SP - 1030
EP - 1039
JO - Journal of Computer Science and Technology
JF - Journal of Computer Science and Technology
IS - 5
ER -