Academic Expert Finding in Indonesia using Word Embedding and Document Embedding: A Case Study of Fasilkom UI
Publikation: Bidrag til bog/antologi/rapport › Konferencebidrag i proceedings › Forskning › fagfællebedømt
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.
|Titel||2020 8th International Conference on Information and Communication Technology, ICoICT 2020|
|Forlag||Institute of Electrical and Electronics Engineers Inc.|
|Status||Udgivet - jun. 2020|
|Begivenhed||8th International Conference on Information and Communication Technology, ICoICT 2020 - Yogyakarta, Indonesien|
Varighed: 24 jun. 2020 → 26 jun. 2020
|Konference||8th International Conference on Information and Communication Technology, ICoICT 2020|
|Periode||24/06/2020 → 26/06/2020|
|Sponsor||IEEE Indonesia Section, IEEE Signal Processing Society Indonesia Chapter|
|Navn||2020 8th International Conference on Information and Communication Technology, ICoICT 2020|
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