Show simple item record

dc.contributor.authorSwain, Matthew Cen
dc.contributor.authorCole, Jacquien
dc.date.accessioned2017-08-16T09:30:44Z
dc.date.available2017-08-16T09:30:44Z
dc.date.issued2016-10-24en
dc.identifier.issn1549-9596
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/266462
dc.description.abstractThe emergence of "big data" initiatives has led to the need for tools that can automatically extract valuable chemical information from large volumes of unstructured data, such as the scientific literature. Since chemical information can be present in figures, tables, and textual paragraphs, successful information extraction often depends on the ability to interpret all of these domains simultaneously. We present a complete toolkit for the automated extraction of chemical entities and their associated properties, measurements, and relationships from scientific documents that can be used to populate structured chemical databases. Our system provides an extensible, chemistry-aware, natural language processing pipeline for tokenization, part-of-speech tagging, named entity recognition, and phrase parsing. Within this scope, we report improved performance for chemical named entity recognition through the use of unsupervised word clustering based on a massive corpus of chemistry articles. For phrase parsing and information extraction, we present the novel use of multiple rule-based grammars that are tailored for interpreting specific document domains such as textual paragraphs, captions, and tables. We also describe document-level processing to resolve data interdependencies and show that this is particularly necessary for the autogeneration of chemical databases since captions and tables commonly contain chemical identifiers and references that are defined elsewhere in the text. The performance of the toolkit to correctly extract various types of data was evaluated, affording an F-score of 93.4%, 86.8%, and 91.5% for extracting chemical identifiers, spectroscopic attributes, and chemical property attributes, respectively; set against the CHEMDNER chemical name extraction challenge, ChemDataExtractor yields a competitive F-score of 87.8%. All tools have been released under the MIT license and are available to download from http://www.chemdataextractor.org .
dc.languageengen
dc.language.isoenen
dc.titleChemDataExtractor: A Toolkit for Automated Extraction of Chemical Information from the Scientific Literature.en
dc.typeArticle
prism.issueIdentifier10en
prism.publicationDate2016en
prism.publicationNameJ Chem Inf Modelen
prism.volume56en
dc.identifier.doi10.17863/CAM.10935
dcterms.dateAccepted2016-09-26en
rioxxterms.versionofrecord10.1021/acs.jcim.6b00207en
rioxxterms.versionAMen
rioxxterms.licenseref.urihttp://www.rioxx.net/licenses/all-rights-reserveden
rioxxterms.licenseref.startdate2016-10-24en
dc.contributor.orcidCole, Jacqui [0000-0002-1552-8743]
dc.identifier.eissn1549-960X
rioxxterms.typeJournal Article/Reviewen
pubs.funder-project-idIsaac Newton Trust (1507(l))
cam.issuedOnline2016-09-26en
rioxxterms.freetoread.startdate2018-08-24


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record