Publication Type
Conference Proceeding Article
Version
publishedVersion
Publication Date
6-2015
Abstract
This paper describes QCRI’s participation in SemEval-2015 Task 3 “Answer Selection in Community Question Answering”, which targeted real-life Web forums, and was offered in both Arabic and English. We apply a supervised machine learning approach considering a manifold of features including among others word n-grams, text similarity, sentiment analysis, the presence of specific words, and the context of a comment. Our approach was the best performing one in the Arabic subtask and the third best in the two English subtasks
Discipline
Databases and Information Systems
Research Areas
Data Science and Engineering
Publication
Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015)
First Page
203
Last Page
209
Identifier
10.18653/v1/S15-2036
Publisher
Association for Computational Linguistics
City or Country
Denver, Colorado, USA
Citation
NICOSIA, Massimo; FILICE, Simone; BARRON-CEDENO, Alberto; SALEH, Iman; MUBARAK, Hamdy; GAO, Wei; NAKOV, Preslav; MARTINO, Giovanni Da San; MOSCHITTI, Alessandro; DARWISH, Kareem; MARQUZ, Lluis Marquz; JOTY, Shafiq; and MAGDY, Walid Magdy.
QCRI: Answer selection for community question answering - Experiment for Arabic and English. (2015). Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015). 203-209.
Available at: https://ink.library.smu.edu.sg/sis_research/4579
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
http://doi.org/10.18653/v1/S15-2036