Publication Type

Conference Proceeding Article

Version

acceptedVersion

Publication Date

12-2024

Abstract

Expert finding systems allow ones to find individuals who have expertise in specific fields or domains. Traditional expert finding are mostly based on topic modeling or keyword search methods that are limited in their capability to encode contextual knowledge from natural language. To address the limitation, this paper presents Neural Expert Finder (NEF), a novel method that takes a transfer learning approach based on transformer encoder networks to leverage the rich seman-tic and syntactic patterns of language encoded in pre-trained language models (PLMs). We propose a self-supervised learning approach utilizing contrastive training using both positive and automatically generated negative samples to fine-tune the PLMs to realize NEF. In addition, we also contribute a new benchmark data set for expert finding named SGComp, curated from experts' university and Google Scholar profiles. Our empirical evaluations demonstrate that the proposed method can effectively capture contextual representations and improve the retrieval of experts most relevant to their corresponding research areas. Both SGComp and three domain specific public data sets are utilized to compare NEF against ExpFinder nVSM, a state-of-the-art (SOTA) system in expert finding, and the results demonstrate consistent better performance of the proposed NEF.

Keywords

Expert finding systems, Transfer learning, Transformer encoder networks, Natural language processing

Discipline

Artificial Intelligence and Robotics | Computer Sciences

Research Areas

Data Science and Engineering

Publication

2024 IEEE/WIC International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT): Bangkok, December 9-12: Proceedings

First Page

38

Last Page

45

ISBN

9798331504946

Identifier

10.1109/WI-IAT62293.2024.00014

Publisher

IEEE

City or Country

Piscataway, NJ

Copyright Owner and License

Authors

Additional URL

https://doi.org/10.1109/WI-IAT62293.2024.00014

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