TY - GEN
T1 - Biological question answering with syntactic and semantic feature matching and an improved mean reciprocal ranking measurement
AU - Lin, Ryan T.K.
AU - Chiu, Justin Liang Te
AU - Dai, Hong Jei
AU - Day, Min Yuh
AU - Tsai, Richard Tzong Han
AU - Hsu, Wen Lian
PY - 2008
Y1 - 2008
N2 - Specific information on biomolecular events such as protein-protein and gene-protein interactions is essential for molecular biology researchers. However, the results derived by current keyword-based information retrieval engine contain a great deal of noisy information, which forces biologists to use a combination of several keywords to locate information. To resolve this problem,, we propose a question answering (QA) system that offers more efficient and user-friendly ways to retrieve desired information. In addition, QA system measurements may suffer from the same score problem, so the evaluation of a QA system may be unfair. An improved mean reciprocal rank (MRR) measurement, mean average reciprocal rank (MARR), and an efficient formula to reduce the computational complexity of the MARR are proposed to address the same score problem. With our syntactic and semantic features, our system achieves a Top-1 MARR of 74.11% and Top-5 MARR of 76.68%. Compared to the baseline system, Top-1 MARR and Top-5 MARR increase by 16.17% and 18.61% respectively.
AB - Specific information on biomolecular events such as protein-protein and gene-protein interactions is essential for molecular biology researchers. However, the results derived by current keyword-based information retrieval engine contain a great deal of noisy information, which forces biologists to use a combination of several keywords to locate information. To resolve this problem,, we propose a question answering (QA) system that offers more efficient and user-friendly ways to retrieve desired information. In addition, QA system measurements may suffer from the same score problem, so the evaluation of a QA system may be unfair. An improved mean reciprocal rank (MRR) measurement, mean average reciprocal rank (MARR), and an efficient formula to reduce the computational complexity of the MARR are proposed to address the same score problem. With our syntactic and semantic features, our system achieves a Top-1 MARR of 74.11% and Top-5 MARR of 76.68%. Compared to the baseline system, Top-1 MARR and Top-5 MARR increase by 16.17% and 18.61% respectively.
UR - http://www.scopus.com/inward/record.url?scp=52349088704&partnerID=8YFLogxK
U2 - 10.1109/IRI.2008.4583027
DO - 10.1109/IRI.2008.4583027
M3 - 會議論文篇章
AN - SCOPUS:52349088704
SN - 9781424426607
T3 - 2008 IEEE International Conference on Information Reuse and Integration, IEEE IRI-2008
SP - 184
EP - 189
BT - Proceedings - CIS Workshops 2007, 2007 International Conference on Computational Intelligence and Security Workshops, CISW 2007
T2 - 2008 IEEE International Conference on Information Reuse and Integration, IEEE IRI-2008
Y2 - 13 July 2008 through 15 July 2008
ER -