Bootstrapping a historical commodities lexicon with SKOS and DBpedia

Ewan Klein, Beatrice Alex, Jim Clifford

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract / Description of output

Named entity recognition for novel domains can be challenging in the absence of suitable training materials for machine-learning or lexicons and gazetteers for term look-up. We describe an approach that starts from a small, manually created word list of commodities traded in the nineteenth century, and then uses semantic web techniques to augment the list by an order of magnitude, drawing on data stored in DBpedia. This work was conducted during the Trading Consequences project on text mining and visualisation of historical documents for the study of global trading in the British empire.
Original languageEnglish
Title of host publicationProceedings of the 8th Workshop on Language Technology for Cultural Heritage, Social Sciences, and Humanities (LaTeCH)
Place of PublicationGothenburg, Sweden
PublisherAssociation for Computational Linguistics
Number of pages9
Publication statusPublished - 1 Apr 2014


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  • Trading Consequences

    Klein, E.



    Project: Research

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