title: Annotated chemical patent corpus: a gold standard for text mining.
abstract: Exploring the chemical and biological space covered by patent applications is crucial in early-stage medicinal chemistry activities. Patent analysis can provide understanding of compound prior art, novelty checking, validation of biological assays, and identification of new starting points for chemical exploration. Extracting chemical and biological entities from patents through manual extraction by expert curators can take substantial amount of time and resources. Text mining methods can help to ease this process. To validate the performance of such methods, a manually annotated patent corpus is essential. In this study we have produced a large gold standard chemical patent corpus. We developed annotation guidelines and selected 200 full patents from the World Intellectual Property Organization, United States Patent and Trademark Office, and European Patent Office. The patents were pre-annotated automatically and made available to four independent annotator groups each consisting of two to ten annotators. The annotators marked chemicals in different subclasses, diseases, targets, and modes of action. Spelling mistakes and spurious line break due to optical character recognition errors were also annotated. A subset of 47 patents was annotated by at least three annotator groups, from which harmonized annotations and inter-annotator agreement scores were derived. One group annotated the full set. The patent corpus includes 400,125 annotations for the full set and 36,537 annotations for the harmonized set. All patents and annotated entities are publicly available at www.biosemantics.org.
authors.affiliation: Department of Medical Informatics, Erasmus University Medical Centre, Rotterdam, The Netherlands.
authors.forename: Saber A
authors.initials: SA
authors.lastname: Akhondi
authors.affiliation: Fraunhofer-Institute for Algorithms and Scientific Computing (SCAI), Fraunhofer-Gesellschaft, Sankt Augustin, Germany.
authors.forename: Alexander G
authors.initials: AG
authors.lastname: Klenner
authors.affiliation: RIA Medicinal Chemistry, AstraZeneca R&D Mölndal, Mölndal, Sweden.
authors.forename: Christian
authors.initials: C
authors.lastname: Tyrchan
authors.affiliation: GVK Biosciences Private Limited, Hyderabad, India.
authors.forename: Anil K
authors.initials: AK
authors.lastname: Manchala
authors.affiliation: GVK Biosciences Private Limited, Hyderabad, India.
authors.forename: Kiran
authors.initials: K
authors.lastname: Boppana
authors.affiliation: NextMove Software Ltd, Cambridge, England.
authors.forename: Daniel
authors.initials: D
authors.lastname: Lowe
authors.affiliation: Fraunhofer-Institute for Algorithms and Scientific Computing (SCAI), Fraunhofer-Gesellschaft, Sankt Augustin, Germany.
authors.forename: Marc
authors.initials: M
authors.lastname: Zimmermann
authors.affiliation: GVK Biosciences Private Limited, Hyderabad, India.
authors.forename: Sarma A R P
authors.initials: SA
authors.lastname: Jagarlapudi
authors.affiliation: NextMove Software Ltd, Cambridge, England.
authors.forename: Roger
authors.initials: R
authors.lastname: Sayle
authors.affiliation: Department of Medical Informatics, Erasmus University Medical Centre, Rotterdam, The Netherlands.
authors.forename: Jan A
authors.initials: JA
authors.lastname: Kors
authors.affiliation: Chemistry Innovation Centre, AstraZeneca R&D Mölndal, Mölndal, Sweden.
authors.forename: Sorel
authors.initials: S
authors.lastname: Muresan
mesh_terms: D019985:Benchmarking; D066289:Data Curation; D057225:Data Mining / Q000592:standards*; D009323:Natural Language Processing; D010330:Patents as Topic; D018875:Vocabulary, Controlled