TY - GEN
T1 - Exploring the context of lexical functions
AU - Kolesnikova, Olga
AU - Gelbukh, Alexander
N1 - Publisher Copyright:
© 2018, Springer Nature Switzerland AG.
PY - 2018
Y1 - 2018
N2 - We explore the context of verb-noun collocations using a corpus of the Excelsior newspaper issues in Spanish. Our purpose is to understand to what extent the context is able to distinguish the semantics of collocations represented by lexical functions of the Meaning-Text Theory. For experiments, four lexical functions were chosen: Oper1, Real1, CausFunc0, and CausFunc1. We inspected different parts of the eight-word window context: the left context, the right context, and both the left and right context. These contexts were retrieved from the original corpus as well as from the same corpus after stopwords deletion. For the vector representation of the context, word counts and tf-idf of words were used. To estimate the ability of the context to predict lexical functions, we used various machine-learning techniques. The best F-measure of 0.65 was achieved for predicting Real1 by Gaussian Naïve Bayes using the left context without stopwords and word counts as features in vectors.
AB - We explore the context of verb-noun collocations using a corpus of the Excelsior newspaper issues in Spanish. Our purpose is to understand to what extent the context is able to distinguish the semantics of collocations represented by lexical functions of the Meaning-Text Theory. For experiments, four lexical functions were chosen: Oper1, Real1, CausFunc0, and CausFunc1. We inspected different parts of the eight-word window context: the left context, the right context, and both the left and right context. These contexts were retrieved from the original corpus as well as from the same corpus after stopwords deletion. For the vector representation of the context, word counts and tf-idf of words were used. To estimate the ability of the context to predict lexical functions, we used various machine-learning techniques. The best F-measure of 0.65 was achieved for predicting Real1 by Gaussian Naïve Bayes using the left context without stopwords and word counts as features in vectors.
KW - Context representation
KW - Lexical functions
KW - Natural language processing
KW - Verb-noun collocations
UR - http://www.scopus.com/inward/record.url?scp=85059942036&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-04497-8_5
DO - 10.1007/978-3-030-04497-8_5
M3 - Contribución a la conferencia
SN - 9783030044961
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 57
EP - 69
BT - Advances in Computational Intelligence - 17th Mexican International Conference on Artificial Intelligence, MICAI 2018, Proceedings
A2 - Batyrshin, Ildar
A2 - de Lourdes Martínez-Villaseñor, María
A2 - Ponce Espinosa, Hiram Eredín
PB - Springer Verlag
T2 - 17th Mexican International Conference on Artificial Intelligence, MICAI 2018
Y2 - 22 October 2018 through 27 October 2018
ER -