Paper Info
Title
Biological question answering with syntactic and semantic feature matching and an improved mean reciprocal ranking measurement.
Abstract
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.
Year
DOI
Venue
2008
10.1109/IRI.2008.4583027
IRI
Keywords
Field
DocType
feature extraction,information retrieval,mean reciprocal rank,computational complexity,data mining,information science,tuning,molecular biology,biology,question answering,protein engineering,engines,rna,dna,computer science,proteins,accuracy
Data mining,Reciprocal,Question answering,Ranking,Computer science,Feature extraction,Specific-information,Mean reciprocal rank,Artificial intelligence,Semantic feature,Machine learning,Computational complexity theory
Conference
ISBN
Citations 
PageRank 
978-1-4244-2660-7
4
0.42
References 
Authors
12
6
Name
Order
Citations
PageRank
Ryan T. K. Lin1312.47
Justin Liang-Te Chiu2151.32
Hong-Jie Dai328821.58
Min-Yuh Day419829.24
Richard Tzong-Han Tsai571454.89
Wen-Lian Hsu61701198.40