Semantic-Based Query Expansion for Academic Expert Finding
Publikation: Bidrag til bog/antologi/rapport › Konferencebidrag i proceedings › Forskning › fagfællebedømt
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Semantic-Based Query Expansion for Academic Expert Finding. / Rampisela, Theresia V.; Yulianti, Evi.
2020 International Conference on Asian Language Processing, IALP 2020. red. / Yanfeng Lu; Minghui Dong; Lay-Ki Soon; Keng Hoon Gan. Institute of Electrical and Electronics Engineers Inc., 2020. s. 34-39 9310492 (2020 International Conference on Asian Language Processing, IALP 2020).Publikation: Bidrag til bog/antologi/rapport › Konferencebidrag i proceedings › Forskning › fagfællebedømt
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TY - GEN
T1 - Semantic-Based Query Expansion for Academic Expert Finding
AU - Rampisela, Theresia V.
AU - Yulianti, Evi
N1 - Funding Information: ACKNOWLEDGMENT This work is supported by the Publikasi Ilmiah Terindeks Internasional (PUTI) Prosiding Universitas Indonesia 2020 grant (NKB-874/UN2.RST/HKP.05.00/2020). Publisher Copyright: © 2020 IEEE.
PY - 2020/12/4
Y1 - 2020/12/4
N2 - 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.
AB - 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.
KW - component
KW - formatting
KW - insert(key words)
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UR - http://www.scopus.com/inward/record.url?scp=85099886548&partnerID=8YFLogxK
U2 - 10.1109/IALP51396.2020.9310492
DO - 10.1109/IALP51396.2020.9310492
M3 - Article in proceedings
AN - SCOPUS:85099886548
T3 - 2020 International Conference on Asian Language Processing, IALP 2020
SP - 34
EP - 39
BT - 2020 International Conference on Asian Language Processing, IALP 2020
A2 - Lu, Yanfeng
A2 - Dong, Minghui
A2 - Soon, Lay-Ki
A2 - Gan, Keng Hoon
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2020 International Conference on Asian Language Processing, IALP 2020
Y2 - 4 December 2020 through 6 December 2020
ER -
ID: 320796223