Abstract

In this paper,we describe how we integrated an ar- tificial intelligence(AI)system into the PubMed search website using augmented browsing tech- nology.Our system dynamically enriches the PubMed search results displayed in a user's browser with semantic annotation provided by several nat- ural language processing(NLP)subsystems,in- cluding a sentence splitter,a part-of-speech tagger, a named entity recognizer,a section categorizer and a gene normalizer(GN).After our system is in- stalled,the PubMed search results page is modified on the fly to categorize sections and provide addi- tional information on gene and gene products iden- tified by our NLP subsystems.In addition,GN in- volves 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 experi- mental results show that our joint model outper- forms a baseline system that executes the three steps separately.The developed system is available at https://sites.google.com/site/pubmedannotationtool 4ijcai/home