TY - GEN
T1 - Learning co-relations of plausible verb arguments with a WSM and a distributional thesaurus
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 second author is currently a JSPS fellow.
PY - 2009
Y1 - 2009
N2 - We propose a model based on the Word Space Model for calculating the plausibility of candidate arguments given one verb and one argument. The resulting information can be used in co-reference resolution, zero-pronoun resolution or syntactic ambiguity tasks. Previous work such as Selectional Preferences or Semantic Frames acquisition focuses on this task using supervised resources, or predicting arguments independently from each other. On this work we explore the extraction of plausible arguments considering their co-relation, and using no more information than that provided by the dependency parser. This creates a data sparseness problem alleviated by using a distributional thesaurus built from the same data for smoothing. We compare our model with the traditional PLSI method.
AB - We propose a model based on the Word Space Model for calculating the plausibility of candidate arguments given one verb and one argument. The resulting information can be used in co-reference resolution, zero-pronoun resolution or syntactic ambiguity tasks. Previous work such as Selectional Preferences or Semantic Frames acquisition focuses on this task using supervised resources, or predicting arguments independently from each other. On this work we explore the extraction of plausible arguments considering their co-relation, and using no more information than that provided by the dependency parser. This creates a data sparseness problem alleviated by using a distributional thesaurus built from the same data for smoothing. We compare our model with the traditional PLSI method.
UR - http://www.scopus.com/inward/record.url?scp=78651259706&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-10268-4_43
DO - 10.1007/978-3-642-10268-4_43
M3 - Contribución a la conferencia
SN - 3642102670
SN - 9783642102677
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 363
EP - 370
BT - Progress in Pattern Recognition, Image Analysis, Computer Vision and Applications - 14th Iberoamerican Conference on Pattern Recognition, CIARP 2009, Proceedings
T2 - 14th Iberoamerican Conference on Pattern Recognition, CIARP 2009
Y2 - 15 November 2009 through 18 November 2009
ER -