Work

Answer Selection in Arabic Community Question Answering: A Feature-Rich Approach

Year: 2015

Type: conference-paper

Abstract: The task of answer selection in community question answering consists of identifying pertinent answers from a pool of user-generated comments related to a question.The recent SemEval-2015 introduced a shared task on community question answering, providing a corpus and evaluation scheme.In this paper we address the problem of answer selection in Arabic.Our proposed model includes a manifold of features including lexical and semantic similarities, vector representations, and rankings.We investigate the contribution of each set of features in a supervised setting.We show that employing a feature combination by means of a linear support vector machine achieves a better performance than that of the competition winner (F 1 of 79.25 compared to 78.55).

Source: Arabic Natural Language Processing Workshop (WANLP)

DOI: 10.18653/v1/w15-3223

Authors: Yonatan Belinkov, Alberto Barrón-Cedeño, Hamdy Mubarak

Institutions: MIT Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Qatar Foundation, Hamad bin Khalifa University

Language: English

Topic: Expert finding and Q&A systems

Subfield: Information Systems

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

Funders: Qatar Foundation, Hamad Bin Khalifa University, Khalifa University of Science, Technology and Research

OpenAlex ID: W2251866374