TY - GEN
T1 - Dependency language modeling using KNN and PLSI
AU - Calvo, Hiram
AU - Inui, Kentaro
AU - Matsumoto, Yuji
N1 - Funding Information:
We thank the support of Mexican Government (SNI, SIP-IPN, COFAA-IPN, and PIFI-IPN), CONACYT; and the Japanese Government; the first author is currently a JSPS fellow.
PY - 2009
Y1 - 2009
N2 - In this paper we present a comparison of two language models based on dependency triples. We explore using the verb only for predicting the most plausible argument as in selectional preferences, as well as using both the verb and argument for predicting another argument. This latter causes a problem of data sparseness that must be solved by different techniques for data smoothing. Based on our results on the K-Nearest Neighbor model (KNN) algorithm we conclude that adding more information is useful for attaining higher precision, while the PLSI model was inconveniently sensitive to this information, yielding better results for the simpler model (using the verb only). Our results suggest that combining the strengths of both algorithms would provide best results.
AB - In this paper we present a comparison of two language models based on dependency triples. We explore using the verb only for predicting the most plausible argument as in selectional preferences, as well as using both the verb and argument for predicting another argument. This latter causes a problem of data sparseness that must be solved by different techniques for data smoothing. Based on our results on the K-Nearest Neighbor model (KNN) algorithm we conclude that adding more information is useful for attaining higher precision, while the PLSI model was inconveniently sensitive to this information, yielding better results for the simpler model (using the verb only). Our results suggest that combining the strengths of both algorithms would provide best results.
UR - http://www.scopus.com/inward/record.url?scp=70549088107&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-05258-3_12
DO - 10.1007/978-3-642-05258-3_12
M3 - Contribución a la conferencia
SN - 3642052576
SN - 9783642052576
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 136
EP - 144
BT - MICAI 2009
T2 - 8th Mexican International Conference on Artificial Intelligence, MICAI 2009
Y2 - 9 November 2009 through 13 November 2009
ER -