Community Question Answering Entity Linking via Leveraging Auxiliary Data

TL;DR

This paper introduces a new entity linking task for Community Question Answering platforms, leveraging auxiliary data like answers, tags, and user info, and proposes a transformer-based framework that outperforms existing methods.

Contribution

It defines the CQA entity linking task and develops a novel transformer-based approach that effectively utilizes auxiliary data for improved linking accuracy.

Findings

Our framework outperforms state-of-the-art methods on CQAEL dataset.

Auxiliary data significantly enhances entity linking performance.

Transformer-based model effectively integrates multiple data sources.

Abstract

Community Question Answering (CQA) platforms contain plenty of CQA texts (i.e., questions and answers corresponding to the question) where named entities appear ubiquitously. In this paper, we define a new task of CQA entity linking (CQAEL) as linking the textual entity mentions detected from CQA texts with their corresponding entities in a knowledge base. This task can facilitate many downstream applications including expert finding and knowledge base enrichment. Traditional entity linking methods mainly focus on linking entities in news documents, and are suboptimal over this new task of CQAEL since they cannot effectively leverage various informative auxiliary data involved in the CQA platform to aid entity linking, such as parallel answers and two types of meta-data (i.e., topic tags and users). To remedy this crucial issue, we propose a novel transformer-based framework to effectively harness the knowledge delivered by different kinds of auxiliary data to promote the linking performance. We validate the superiority of our framework through extensive experiments over a newly released CQAEL data set against state-of-the-art entity linking methods.