Matches in Ruben’s data for { <https://dx.doi.org/10.1109/BigData.2016.7840981> ?p ?o }
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- BigData.2016.7840981 author simon_hengchen.
- BigData.2016.7840981 author seth_van_hooland.
- BigData.2016.7840981 author mathias_coeckelbergs.
- BigData.2016.7840981 author me.
- BigData.2016.7840981 author me.
- BigData.2016.7840981 creator simon_hengchen.
- BigData.2016.7840981 creator seth_van_hooland.
- BigData.2016.7840981 creator mathias_coeckelbergs.
- BigData.2016.7840981 creator me.
- BigData.2016.7840981 creator me.
- BigData.2016.7840981 about proof.
- BigData.2016.7840981 about Research.
- BigData.2016.7840981 about Metadata.
- BigData.2016.7840981 author simon_hengchen.
- BigData.2016.7840981 author seth_van_hooland.
- BigData.2016.7840981 author mathias_coeckelbergs.
- BigData.2016.7840981 author me.
- BigData.2016.7840981 author me.
- BigData.2016.7840981 coparticipatesWith simon_hengchen.
- BigData.2016.7840981 coparticipatesWith seth_van_hooland.
- BigData.2016.7840981 coparticipatesWith mathias_coeckelbergs.
- BigData.2016.7840981 coparticipatesWith me.
- BigData.2016.7840981 coparticipatesWith me.
- BigData.2016.7840981 type ScholarlyArticle.
- BigData.2016.7840981 type Article.
- BigData.2016.7840981 type Document.
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- BigData.2016.7840981 P50 simon_hengchen.
- BigData.2016.7840981 P50 seth_van_hooland.
- BigData.2016.7840981 P50 mathias_coeckelbergs.
- BigData.2016.7840981 P50 me.
- BigData.2016.7840981 P50 me.
- BigData.2016.7840981 maker simon_hengchen.
- BigData.2016.7840981 maker seth_van_hooland.
- BigData.2016.7840981 maker mathias_coeckelbergs.
- BigData.2016.7840981 maker me.
- BigData.2016.7840981 maker me.
- BigData.2016.7840981 title "Exploring archives with probabilistic models: Topic Modelling for the valorisation of digitised archives of the European Commission".
- BigData.2016.7840981 isPartOf proceedings_of_the_ieee_international_conference_on_big_data.
- BigData.2016.7840981 name "Exploring archives with probabilistic models: Topic Modelling for the valorisation of digitised archives of the European Commission".
- BigData.2016.7840981 label "Exploring archives with probabilistic models: Topic Modelling for the valorisation of digitised archives of the European Commission".
- BigData.2016.7840981 name "Exploring archives with probabilistic models: Topic Modelling for the valorisation of digitised archives of the European Commission".
- BigData.2016.7840981 topic proof.
- BigData.2016.7840981 topic Research.
- BigData.2016.7840981 topic Metadata.
- BigData.2016.7840981 subject proof.
- BigData.2016.7840981 subject Research.
- BigData.2016.7840981 subject Metadata.
- BigData.2016.7840981 authorList b0_b2379.
- BigData.2016.7840981 topic proof.
- BigData.2016.7840981 topic Research.
- BigData.2016.7840981 topic Metadata.
- BigData.2016.7840981 abstract "Topic Modelling (TM) has gained momentum over the last few years within the humanities to analyze topics represented in large volumes of full text. This paper proposes an experiment with the usage of TM based on a large subset of digitized archival holdings of the European Commission (EC). Currently, millions of scanned and OCR’ed files are available and hold the potential to significantly change the way historians of the construction and evolution of the European Union can perform their research. However, due to a lack of resources, only minimal metadata are available on a file and document level, seriously undermining the accessibility of this archival collection. The article explores in an empirical manner the possibilities and limits of TM to automatically extract key concepts from a large body of documents spanning multiple decades. By mapping the topics to headings of the EUROVOC thesaurus, the proof of concept described in this paper offers the future possibility to represent the identified topics with the help of a hierarchical search interface for end-users.".
- BigData.2016.7840981 datePublished "2016".
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- BigData.2016.7840981 number "3245".
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