Please use this identifier to cite or link to this item:
http://hdl.handle.net/10174/24987
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| Title: | Improving Personalized Consumer Health Search |
| Authors: | Yang, Hua
Gonçalves, Teresa |
| Editors: | Cappelato, L.
Ferro, N.
Niw, J.N.
Soulier, L. |
| Keywords: | health information search
learning to rank
query expansion
UMLS
word vectors |
| Issue Date: | 2018 |
| Publisher: | CEUR |
| Citation: | Hua Yang and Teresa Gonçalves. Improving personalized consumer health search: Note-
book for ehealth at clef 2018. In Linda Cappellato, Nicola Ferro, Jian-Yun Nie, and Laure Soulier, editors, Working Notes of CLEF 2018 - Conference and Labs of the Evaluation
Forum, Avignon, France, September 10-14, 2018. |
| Abstract: | CLEF 2018 eHealth Consumer Health Search task aims to investigate the effectiveness of the information retrieval systems in providing health information to common health consumers. Compared to previous years, this year’s task includes five subtasks and adopts new data corpus and set of queries. This paper presents the work of University of Evora participating in two subtasks: IRtask-1 and IRtask-2. It explores the use of learning to rank techniques as well as query expan-
sion approaches. A number of field based features are used for training a learning to rank model and a medical concept model proposed in previous work is re-employed for this year’s new task. Word vectors and
UMLS are used as query expansion sources. Four runs were submitted to each task accordingly. |
| URI: | http://hdl.handle.net/10174/24987 |
| Type: | article |
| Appears in Collections: | INF - Publicações - Artigos em Revistas Internacionais Com Arbitragem Científica
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