TY - GEN
T1 - Enhancing search results with semantic annotation using augmented browsing
AU - Dai, Hong Jie
AU - Tsai, Wei Chi
AU - Tsai, Richard Tzong Han
AU - Hsu, Wen Lian
PY - 2011
Y1 - 2011
N2 - In this paper, we describe how we integrated an artificial intelligence (AT) system into the PubMed search website using angmented browsing technology. Our system dynamically enriches the PubMed search results displayed in a user's browser with semantic annotation provided by several natural language processing (NLP) subsystems, including a sentence splitter, a part-of-speech tagger, a named entity recognizer, a section categorizer and a gene normalizer (GN). After our system is installed, the PubMed search results page is modified on the fly to categorize sections and provide additional information on gene and gene products identified by our NLP subsystems. In addition, GN involves three main steps: candidate ID matching, false positive filtering and disambiguation, which are highly dependent on each other. We propose a joint model using a Markov logic network (MLN) to model the dependencies found in GN. The experimental results show that our joint model outerforms a baseline system that executes the three steps separately. The developed system is available at https://sites.google.com/site/pubmedannotationtool4ijcai/home.
AB - In this paper, we describe how we integrated an artificial intelligence (AT) system into the PubMed search website using angmented browsing technology. Our system dynamically enriches the PubMed search results displayed in a user's browser with semantic annotation provided by several natural language processing (NLP) subsystems, including a sentence splitter, a part-of-speech tagger, a named entity recognizer, a section categorizer and a gene normalizer (GN). After our system is installed, the PubMed search results page is modified on the fly to categorize sections and provide additional information on gene and gene products identified by our NLP subsystems. In addition, GN involves three main steps: candidate ID matching, false positive filtering and disambiguation, which are highly dependent on each other. We propose a joint model using a Markov logic network (MLN) to model the dependencies found in GN. The experimental results show that our joint model outerforms a baseline system that executes the three steps separately. The developed system is available at https://sites.google.com/site/pubmedannotationtool4ijcai/home.
UR - http://www.scopus.com/inward/record.url?scp=84881081902&partnerID=8YFLogxK
U2 - 10.5591/978-1-57735-516-8/IJCAI11-403
DO - 10.5591/978-1-57735-516-8/IJCAI11-403
M3 - 會議論文篇章
AN - SCOPUS:84881081902
SN - 9781577355120
T3 - IJCAI International Joint Conference on Artificial Intelligence
SP - 2418
EP - 2423
BT - IJCAI 2011 - 22nd International Joint Conference on Artificial Intelligence
T2 - 22nd International Joint Conference on Artificial Intelligence, IJCAI 2011
Y2 - 16 July 2011 through 22 July 2011
ER -