Academic Expert Finding in Indonesia using Word Embedding and Document Embedding: A Case Study of Fasilkom UI

Theresia V. Rampisela, Evi Yulianti

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

11 Citations (Scopus)

Abstract

Expertise retrieval covers the problems of expert and expertise finding. In academia, expert finding can be beneficial in finding a research partner or a potential thesis supervisor. This research finds the experts in the Faculty of Computer Science in Universitas Indonesia (Fasilkom UI) using the thesis abstract and metadata of Fasilkom UI students. The methods that are used to represent the query and expertise of the lecturers are the combination of word2vec and doc2vec, which are word embedding and document embedding, respectively. Both embeddings are able to model semantic information, which is necessary for solving the problem of vocabulary mismatch in search problems. Our result shows that representing the expertise query with word2vec leads to better performance than using doc2vec. In addition, we also found that generally, the performance of the embedding models is comparable to the standard retrieval model BM25 in retrieving experts using expertise queries in both Indonesian and English languages.

Original languageEnglish
Title of host publication2020 8th International Conference on Information and Communication Technology, ICoICT 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728161426
DOIs
Publication statusPublished - Jun 2020
Event8th International Conference on Information and Communication Technology, ICoICT 2020 - Yogyakarta, Indonesia
Duration: 24 Jun 202026 Jun 2020

Publication series

Name2020 8th International Conference on Information and Communication Technology, ICoICT 2020

Conference

Conference8th International Conference on Information and Communication Technology, ICoICT 2020
Country/TerritoryIndonesia
CityYogyakarta
Period24/06/2026/06/20

Keywords

  • academic expert
  • document embedding
  • expert finding
  • expertise retrieval
  • word embedding

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