title: Enhancement of chemical entity identification in text using semantic similarity validation.
abstract: With the amount of chemical data being produced and reported in the literature growing at a fast pace, it is increasingly important to efficiently retrieve this information. To tackle this issue text mining tools have been applied, but despite their good performance they still provide many errors that we believe can be filtered by using semantic similarity. Thus, this paper proposes a novel method that receives the results of chemical entity identification systems, such as Whatizit, and exploits the semantic relationships in ChEBI to measure the similarity between the entities found in the text. The method assigns a single validation score to each entity based on its similarities with the other entities also identified in the text. Then, by using a given threshold, the method selects a set of validated entities and a set of outlier entities. We evaluated our method using the results of two state-of-the-art chemical entity identification tools, three semantic similarity measures and two text window sizes. The method was able to increase precision without filtering a significant number of correctly identified entities. This means that the method can effectively discriminate the correctly identified chemical entities, while discarding a significant number of identification errors. For example, selecting a validation set with 75% of all identified entities, we were able to increase the precision by 28% for one of the chemical entity identification tools (Whatizit), maintaining in that subset 97% the correctly identified entities. Our method can be directly used as an add-on by any state-of-the-art entity identification tool that provides mappings to a database, in order to improve their results. The proposed method is included in a freely accessible web tool at www.lasige.di.fc.ul.pt/webtools/ice/.
authors: Departamento de Informtica, Faculdade de Cincias, Universidade de Lisboa, Lisboa, Portugal. tgrego@fc.ul.pt
authors: Tiago
authors: T
authors: Grego
authors: Francisco M
authors: FM
authors: Couto
mesh_terms: D057225:Data Mining / Q000379:methods* / Q000592:standards; D062126:Databases, Chemical; D010330:Patents as Topic; D012015:Reference Standards; D015203:Reproducibility of Results; D012660:Semantics
references: BMC Bioinformatics. 2009 Oct 08;10:326
references: 19814812
references: Brief Bioinform. 2005 Sep;6(3):239-51
references: 16212772
references: PLoS Comput Biol. 2010 Sep 23;6(9):
references: 20885779
references: Mol Inform. 2011 Jun;30(6-7):506-19
references: 27467152
references: PLoS Comput Biol. 2009 Dec;5(12):e1000597
references: 20041219
references: Neuroinformatics. 2005;3(1):15-48
references: 15897615
references: Drug Discov Today. 2006 Apr;11(7-8):315-25
references: 16580973
references: Bioinformatics. 2011 May 1;27(9):1341-2
references: 21414988
references: J Biol. 2006;5(4):11
references: 16762047
references: Drug Discov Today. 2010 Dec;15(23-24):1052-7
references: 20970519
references: BMC Bioinformatics. 2008 Apr 29;9 Suppl 5:S4
references: 18460186
references: Drug Discov Today Technol. 2005 Autumn;2(3):217-24
references: 24981939
references: PLoS Biol. 2004 Nov;2(11):e309
references: 15383839
references: Bioinformatics. 2008 Jan 15;24(2):296-8
references: 18006544
references: PLoS Comput Biol. 2009 Jul;5(7):e1000443
references: 19649320
references: Genome Biol. 2008;9 Suppl 2:S2
references: 18834493
references: Drug Discov Today. 2006 Jan;11(1-2):35-42
references: 16478689
references: Nucleic Acids Res. 2010 Jan;38(Database issue):D249-54
references: 19854951
references: ISRN Bioinform. 2012 Feb 15;2012:619427
references: 25937941
references: Bioinformatics. 2009 Nov 15;25(22):2983-91
references: 19759196
references: Drug Discov Today Technol. 2005 Autumn;2(3):235-40
references: 24981941
references: J Biomed Inform. 2004 Feb;37(1):43-53
references: 15016385
references: Brief Bioinform. 2008 Nov;9(6):466-78
references: 19060303