2 research outputs found
Annotated chemical patent corpus: A gold standard for text mining
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, t
The CHEMDNER corpus of chemicals and drugs and its annotation principles
The automatic extraction of chemical information from text requires the recognition of chemical entity mentions as one
of its key steps. When developing supervised named entity recognition (NER) systems, the availability of a large,
manually annotated text corpus is desirable. Furthermore, large corpora permit the robust evaluation and comparison
of different approaches that detect chemicals in documents. We present the CHEMDNER corpus, a collection of 10,000
PubMed abstracts that contain a total of 84,355 chemical entity mentions labeled manually by expert chemistry
literature curators, following annotation guidelines specifically defined for this task. The abstracts of the CHEMDNER
corpus were selected to be representative for all major chemical disciplines. Each of the chemical entity mentions was
manually labeled according to its structure-associated chemical entity mention (SACEM) class: abbreviation, family,
formula, identifier, multiple, systematic and trivial. The difficulty and consistency of tagging chemicals in text was
measured using an agreement study between annotators, obtaining a percentage agreement of 91. For a subset of the
CHEMDNER corpus (the test set of 3,000 abstracts) we provide not only the Gold Standard manual annotations, but also
mentions automatically detected by the 26 teams that participated in the BioCreative IV CHEMDNER chemical mention
recognition task. In addition, we release the CHEMDNER silver standard corpus of automatically extracted mentions
from 17,000 randomly selected PubMed abstracts. A version of the CHEMDNER corpus in the BioC format has been
generated as well. We propose a standard for required minimum information about entity annotations for the
construction of domain specific corpora on chemical and drug entities. The CHEMDNER corpus and annotation
guidelines are available at: http://www.biocreative.org/resources/biocreative-iv/chemdner-corpus