TY - CHAP
T1 - Learning Boolean queries for article quality filtering.
AU - Aphinyanaphongs, Yin
AU - Aliferis, Constantin
PY - 2004
Y1 - 2004
N2 - Prior research has shown that Support Vector Machine models have the ability to identify high quality content-specific articles in the domain of internal medicine. These models, though powerful, cannot be used in Boolean search engines nor can the content of the models be verified via human inspection. In this paper, we use decision trees combined with several feature selection methods to generate Boolean query filters for the same domain and task. The resulting trees are generated automatically and exhibit high performance. The trees are understandable, manageable, and able to be validated by humans. The subsequent Boolean queries are sensible and can be readily used as filters by Boolean search engines.
AB - Prior research has shown that Support Vector Machine models have the ability to identify high quality content-specific articles in the domain of internal medicine. These models, though powerful, cannot be used in Boolean search engines nor can the content of the models be verified via human inspection. In this paper, we use decision trees combined with several feature selection methods to generate Boolean query filters for the same domain and task. The resulting trees are generated automatically and exhibit high performance. The trees are understandable, manageable, and able to be validated by humans. The subsequent Boolean queries are sensible and can be readily used as filters by Boolean search engines.
KW - Artificial Intelligence
KW - Information Storage and Retrieval
KW - Medical Informatics
KW - PubMed
KW - Text Categorization
UR - http://www.scopus.com/inward/record.url?scp=84887117785&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84887117785&partnerID=8YFLogxK
U2 - 10.3233/978-1-60750-949-3-263
DO - 10.3233/978-1-60750-949-3-263
M3 - Chapter
C2 - 15360815
AN - SCOPUS:84887117785
VL - 107
SP - 263
EP - 267
BT - Studies in Health Technology and Informatics
ER -