Spanish verb-noun lexical functions dictionary, version 2 (download).
This dataset was used for classification experiments in the following book and papers:
- Alexander Gelbukh, Olga Kolesnikova. Semantic Analysis of Verbal Collocations with Lexical Functions. ISBN 978-3-642-28770-1; DOI 10.1007/978-3-642-28771-8. Series: Studies in Computational Intelligence N 414, ISSN 1860-949X. Springer, 2013, XI + 146 pp.
- Olga Kolesnikova, Alexander Gelbukh. Semantic relations between collocations—A Spanish case study. Revista Signos, ISSN 0035-0451, Vol. 45, No. 78, pp. 44-59, March, 2012; DOI: 10.4067/S0718-09342012000100003.
- Alexander Gelbukh, Olga Kolesnikova. Multiword Expressions in NLP: General Survey and a Special Case of Verb-Noun Constructions. Book chapter. In: Sivaji Bandyopadhyay, Sudip Kumar Naskar, Asif Ekbal (eds.). Emerging Applications of Natural Language Processing: Concepts and New Research. IGI Global. DOI: 10.4018/978-1-4666-2169-5, ISBN: 978-1-4666-2169-5, 2012.
- Alexander Gelbukh, Olga Kolesnikova. Supervised Machine Learning for Predicting the Meaning of Verb-Noun Combinations in Spanish. MICAI 2010. Lecture Notes in Artificial Intelligence N 6438, ISSN 0302-9743, Springer, 2010, pp. 196–207 (LNCS online)
Any work that uses the dataset should cite the abovementioned sources.
The complete dictionary of Spanish verb-noun lexical functions and the experimental results can be openly accessed as an appendix to the abovementioned book; scroll down to Appendix.
Before downloading these data please contact us (see contact info on www.Gelbukh.com) to see if we have a new version.
Here is a list of lexical functions used in the dataset:
License: free for non-commercial academic purposes. Any publication that benefited from these data must state the origin of the data and cite the abovementioned sources. We will be grateful to you if you let us know of the use of the data and of citing our papers.
Spanish verb-noun lexical functions dictionary, version 1 (we have a newer version, see above). This version was used in the MCPR 2010 paper and probably in some other papers.