Semantic-Based Query Expansion for Academic Expert Finding

Theresia V. Rampisela, Evi Yulianti

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Citations (Scopus)

Abstract

Expert finding in academic domain is useful for many purposes, such as: to find research collaborators, article reviewers, thesis advisors, thesis examiners, etc. This work examines the use of semantic information, i.e. word embedding and document embedding, for query expansion to enhance the effectiveness of expert finding system. This information is utilized to bridge the lexical gap between the query and the expertise evidence of the experts. This semantic-based query expansion approach is then combined with a BM25 retrieval method to find relevant experts to the given query. The results show that our methods consistently outperform the strong retrieval method BM25, the semantic-based retrieval, and query expansion using pseudo relevance feedback method according to all recall- and precision-based measures used in this work. This indicates the effectiveness of our methods in improving the number and the accuracy of relevant experts retrieved.

Original languageEnglish
Title of host publication2020 International Conference on Asian Language Processing, IALP 2020
EditorsYanfeng Lu, Minghui Dong, Lay-Ki Soon, Keng Hoon Gan
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages34-39
Number of pages6
ISBN (Electronic)9781728176895
DOIs
Publication statusPublished - 4 Dec 2020
Event2020 International Conference on Asian Language Processing, IALP 2020 - Kuala Lumpur, Malaysia
Duration: 4 Dec 20206 Dec 2020

Publication series

Name2020 International Conference on Asian Language Processing, IALP 2020

Conference

Conference2020 International Conference on Asian Language Processing, IALP 2020
Country/TerritoryMalaysia
CityKuala Lumpur
Period4/12/206/12/20

Keywords

  • component
  • formatting
  • insert(key words)
  • style
  • styling

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