Document Retrieval for Large Scale Content Analysis using Contextualized Dictionaries

Gregor Wiedemann 1, * Andreas Niekler 1, *
* Auteur correspondant
1 Abteilung Automatische Sprachverarbeitung, Institut für Informatik, Universtität Leipzig
NLP Group | Department of Computer Science University of Leipzig
Abstract : This paper presents a procedure to retrieve subsets of rele- vant documents from large text collections for Content Analysis, e.g. in social sciences. Document retrieval for this purpose needs to take account of the fact that analysts often cannot describe their research objective with a small set of key terms, especially when dealing with theoretical or rather abstract research interests. Instead, it is much easier to de ne a set of paradigmatic documents which re ect topics of interest as well as tar- geted manner of speech. Thus, in contrast to classic information retrieval tasks we employ manually compiled collections of reference documents to compose large queries of several hundred key terms, called dictionar- ies. We extract dictionaries via Topic Models and also use co-occurrence data from reference collections. Evaluations show that the procedure im- proves retrieval results for this purpose compared to alternative methods of key term extraction as well as neglecting co-occurrence data.
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Communication dans un congrès
Terminology and Knowledge Engineering 2014, Jun 2014, Berlin, Germany. 10 p, 2014
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Gregor Wiedemann, Andreas Niekler. Document Retrieval for Large Scale Content Analysis using Contextualized Dictionaries. Terminology and Knowledge Engineering 2014, Jun 2014, Berlin, Germany. 10 p, 2014. 〈hal-01005879〉

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