Enhancing search results with semantic annotation using augmented browsing

Hong Jie Dai, Wei Chi Tsai, Richard Tzong Han Tsai, Wen Lian Hsu

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Scopus citations

Abstract

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.

Original languageEnglish
Title of host publicationIJCAI 2011 - 22nd International Joint Conference on Artificial Intelligence
Pages2418-2423
Number of pages6
DOIs
StatePublished - 2011
Event22nd International Joint Conference on Artificial Intelligence, IJCAI 2011 - Barcelona, Catalonia, Spain
Duration: 16 Jul 201122 Jul 2011

Publication series

NameIJCAI International Joint Conference on Artificial Intelligence
ISSN (Print)1045-0823

Conference

Conference22nd International Joint Conference on Artificial Intelligence, IJCAI 2011
Country/TerritorySpain
CityBarcelona, Catalonia
Period16/07/1122/07/11

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