Deep learning contextual models for prediction of sport event outcome from sportsman’s interviews

Boris Velichkov, Ivan Koychev, Svetla Boytcheva


Abstract
This paper presents an approach for prediction of results for sport events. Usually the sport forecasting approaches are based on structured data. We test the hypothesis that the sports results can be predicted by using natural language processing and machine learning techniques applied over interviews with the players shortly before the sport events. The proposed method uses deep learning contextual models, applied over unstructured textual documents. Several experiments were performed for interviews with players in individual sports like boxing, martial arts, and tennis. The results from the conducted experiment confirmed our initial assumption that an interview from a sportsman before a match contains information that can be used for prediction the outcome from it. Furthermore, the results provide strong evidence in support of our research hypothesis, that is, we can predict the outcome from a sport match analyzing an interview, given before it.
Anthology ID:
R19-1142
Volume:
Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)
Month:
September
Year:
2019
Address:
Varna, Bulgaria
Venue:
RANLP
SIG:
Publisher:
INCOMA Ltd.
Note:
Pages:
1240–1246
Language:
URL:
https://www.aclweb.org/anthology/R19-1142
DOI:
10.26615/978-954-452-056-4_142
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PDF:
http://aclanthology.lst.uni-saarland.de/R19-1142.pdf