UH-PRHLT at SemEval-2016 Task 3: Combining Lexical and Semantic-based Features for Community Question Answering

TL;DR

This paper presents a system for community question answering that combines lexical and semantic features, including distributed word representations and knowledge graphs, achieving top results in SemEval-2016 Task 3.

Contribution

The novel integration of lexical and semantic similarity measures, including BabelNet and FrameNet, for improved community question answering performance.

Findings

Outperformed random and Google search baselines in all subtasks.

Achieved highest results in subtask B among participants.

Demonstrated effectiveness of semantic features in QA tasks.

Abstract

In this work we describe the system built for the three English subtasks of the SemEval 2016 Task 3 by the Department of Computer Science of the University of Houston (UH) and the Pattern Recognition and Human Language Technology (PRHLT) research center - Universitat Polit`ecnica de Val`encia: UH-PRHLT. Our system represents instances by using both lexical and semantic-based similarity measures between text pairs. Our semantic features include the use of distributed representations of words, knowledge graphs generated with the BabelNet multilingual semantic network, and the FrameNet lexical database. Experimental results outperform the random and Google search engine baselines in the three English subtasks. Our approach obtained the highest results of subtask B compared to the other task participants.