Abstract
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.
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The complete list of 737 Spanish verb-noun collocations annotated with 36 lexical functions can be accessed at http://148.204.58.221/okolesnikova/index.php?id=lex/ or http://www.gelbukh.com/lexical-functions.
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Acknowledgements
The research was done under partial support of Mexican Government: SNI, BEIFI-IPN, and SIP-IPN grants 20182119 and 20181792. The work was done when A. Gelbukh was visiting the Research Institute for Information and Language Processing, University of Wolverhampton, on a grant from the Sabbatical Year Program of the CONACYT, Mexico.
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Kolesnikova, O., Gelbukh, A. (2018). Exploring the Context of Lexical Functions. In: Batyrshin, I., Martínez-Villaseñor, M., Ponce Espinosa, H. (eds) Advances in Computational Intelligence. MICAI 2018. Lecture Notes in Computer Science(), vol 11289. Springer, Cham. https://doi.org/10.1007/978-3-030-04497-8_5
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