Paper Info
Title
Enhancing search results with semantic annotation using augmented browsing
Abstract
In this paper, we describe how we integrated an artificial intelligence (AI) system into the PubMed search website using augmented 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 indentified 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 outperforms a baseline system that executes the three steps separately. The developed system is available at https://sites.google.com/site/pubmedannotationtool 4ijcai/home.
Year
DOI
Venue
2011
10.5591/978-1-57735-516-8/IJCAI11-403
IJCAI
Keywords
Field
DocType
Enhancing search result,augmented browsing,system dynamically,joint model,gene normalizer,baseline system,semantic annotation,PubMed search results page,developed system,PubMed search result,PubMed search website,gene product,NLP subsystems
Augmented browsing,Markov logic network,Semantic annotation,Information retrieval,Computer science,On the fly,Filter (signal processing),Named entity,Artificial intelligence,Baseline system,Sentence,Machine learning
Conference
Citations 
PageRank 
References 
1
0.41
11
Authors
4
Name
Order
Citations
PageRank
Hong-Jie Dai128821.58
Wei-Chi Tsai291.96
Richard Tzong-Han Tsai371454.89
Wen-Lian Hsu41701198.40