Community Question Answering Entity Linking via Leveraging Auxiliary Data

• URL: http://arxiv.org/abs/2205.11917v1
• Date: Tue, 24 May 2022 09:25:18 GMT
• Title: Community Question Answering Entity Linking via Leveraging Auxiliary Data
• Authors: Yuhan Li, Wei Shen, Jianbo Gao, Yadong Wang
• Abstract summary: We propose 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. Traditional entity linking methods mainly focus on linking entities in news documents. We propose a novel transformer-based framework to effectively harness the knowledge delivered by different kinds of auxiliary data to promote the linking performance.
• Score: 7.834536363163232
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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