TrentoTeam at SemEval-2017 Task 3: An application of Grice Maxims in Ranking Community Question Answers
| Autor: | Abed Alhakim Freihat, Fausto Giunchiglia, Mohammed R. H. Qwaider |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
Information retrieval
biology Computer science business.industry 02 engineering and technology computer.software_genre SemEval Task (project management) Ranking (information retrieval) Named entity Ranking 020204 information systems biology.animal Similarity (psychology) 0202 electrical engineering electronic engineering information engineering Grice 020201 artificial intelligence & image processing Artificial intelligence business computer Natural language processing |
| Zdroj: | SemEval@ACL |
| DOI: | 10.18653/v1/s17-2043 |
| Popis: | In this paper we present the Tren-toTeam system which participated to thetask 3 at SemEval-2017 (Nakov et al.,2017).We concentrated our work onapplying Grice Maxims(used in manystate-of-the-art Machine learning applica-tions(Vogel et al., 2013; Kheirabadiand Aghagolzadeh, 2012; Dale and Re-iter, 1995; Franke, 2011)) to ranking an-swers of a question by answers relevancy.Particularly, we created a ranker systembased on relevancy scores, assigned by 3main components: Named entity recogni-tion, similarity score, sentiment analysis.Our system obtained a comparable resultsto Machine learning systems. |
| Databáze: | OpenAIRE |
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