TY - GEN
T1 - Supervised machine learning for predicting the meaning of verb-noun combinations in Spanish
AU - Kolesnikova, Olga
AU - Gelbukh, Alexander
N1 - Funding Information:
The work was done under partial support of Mexican Government: SNI, COFAA-IPN, PIFI-IPN, CONACYT grant 50206-H, and SIP-IPN grant 20100773.
PY - 2010
Y1 - 2010
N2 - The meaning of such verb-noun combinations as take care, undertake work, pay attention can be generalized as DO what is designated by the noun. Likewise, the meaning of make a decision, provide support, write a letter can be generalized as MAKE what is designated by the noun. These generalizations represent the meaning of certain groups of verb-noun combinations. We use supervised machine learning algorithms to predict the meanings DO, MAKE, BEGIN, and CONTINUE of previously unseen verb-noun pairs. We evaluate the performance of the applied algorithms on a training set using 10- fold cross-validation technique. The learnt models have also been evaluated on an independent test set and the predictions have been checked manually to determine the accuracy of the classifiers. The obtained results show that supervised machine learning methods achieve significant accuracy and can be used for semantic annotation of verb-noun combinations.
AB - The meaning of such verb-noun combinations as take care, undertake work, pay attention can be generalized as DO what is designated by the noun. Likewise, the meaning of make a decision, provide support, write a letter can be generalized as MAKE what is designated by the noun. These generalizations represent the meaning of certain groups of verb-noun combinations. We use supervised machine learning algorithms to predict the meanings DO, MAKE, BEGIN, and CONTINUE of previously unseen verb-noun pairs. We evaluate the performance of the applied algorithms on a training set using 10- fold cross-validation technique. The learnt models have also been evaluated on an independent test set and the predictions have been checked manually to determine the accuracy of the classifiers. The obtained results show that supervised machine learning methods achieve significant accuracy and can be used for semantic annotation of verb-noun combinations.
KW - lexical functions
KW - meaning representation by means of hypernyms
KW - supervised machine learning
KW - verb-noun combinations
UR - http://www.scopus.com/inward/record.url?scp=78650019495&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-16773-7_17
DO - 10.1007/978-3-642-16773-7_17
M3 - Contribución a la conferencia
SN - 3642167721
SN - 9783642167720
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 196
EP - 207
BT - Advances in Soft Computing - 9th Mexican International Conference on Artificial Intelligence, MICAI 2010, Proceedings
T2 - 9th Mexican International Conference on Artificial Intelligence, MICAI 2010
Y2 - 8 November 2010 through 13 November 2010
ER -