Abstract
We introduce an approach to question answering in the biomedical domain that utilises similarity matching of question/answer pairs in a document, or a set of background documents, to select the best answer to a multiple-choice question. We explored a range of possible similarity matching methods, ranging from simple word overlap, to dependency graph matching, to feature-based vector similarity models that incorporate lexical, syntactic and/or semantic features. We found that while these methods performed reasonably well on a small training set, they did not generalise well to the final test data.
| Language | English |
|---|---|
| Pages | 1-13 |
| Number of pages | 13 |
| Journal | CEUR Workshop Proceedings |
| Volume | 1178 |
| Publication status | Published - 2012 |
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Simple similarity-based question answering strategies for biomedical text. / Martinez, David; MacKinlay, Andrew; Molla-Aliod, Diego; Cavedon, Lawrence; Verspoor, Karin.
In: CEUR Workshop Proceedings, Vol. 1178, 2012, p. 1-13.Research output: Contribution to journal › Conference paper › Research
TY - JOUR
T1 - Simple similarity-based question answering strategies for biomedical text
AU - Martinez, David
AU - MacKinlay, Andrew
AU - Molla-Aliod, Diego
AU - Cavedon, Lawrence
AU - Verspoor, Karin
PY - 2012
Y1 - 2012
N2 - We introduce an approach to question answering in the biomedical domain that utilises similarity matching of question/answer pairs in a document, or a set of background documents, to select the best answer to a multiple-choice question. We explored a range of possible similarity matching methods, ranging from simple word overlap, to dependency graph matching, to feature-based vector similarity models that incorporate lexical, syntactic and/or semantic features. We found that while these methods performed reasonably well on a small training set, they did not generalise well to the final test data.
AB - We introduce an approach to question answering in the biomedical domain that utilises similarity matching of question/answer pairs in a document, or a set of background documents, to select the best answer to a multiple-choice question. We explored a range of possible similarity matching methods, ranging from simple word overlap, to dependency graph matching, to feature-based vector similarity models that incorporate lexical, syntactic and/or semantic features. We found that while these methods performed reasonably well on a small training set, they did not generalise well to the final test data.
UR - http://www.scopus.com/inward/record.url?scp=84922021520&partnerID=8YFLogxK
M3 - Conference paper
VL - 1178
SP - 1
EP - 13
JO - CEUR Workshop Proceedings
T2 - CEUR Workshop Proceedings
JF - CEUR Workshop Proceedings
SN - 1613-0073
ER -