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Mining Multiword Terms from Wikipedia

Mining Multiword Terms from Wikipedia

Silvana Hartmann, György Szarvas, Iryna Gurevych
Copyright: © 2012 |Pages: 33
ISBN13: 9781466601888|ISBN10: 1466601884|EISBN13: 9781466601895
DOI: 10.4018/978-1-4666-0188-8.ch009
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MLA

Hartmann, Silvana, et al. "Mining Multiword Terms from Wikipedia." Semi-Automatic Ontology Development: Processes and Resources, edited by Maria Teresa Pazienza and Armando Stellato, IGI Global, 2012, pp. 226-258. https://doi.org/10.4018/978-1-4666-0188-8.ch009

APA

Hartmann, S., Szarvas, G., & Gurevych, I. (2012). Mining Multiword Terms from Wikipedia. In M. Pazienza & A. Stellato (Eds.), Semi-Automatic Ontology Development: Processes and Resources (pp. 226-258). IGI Global. https://doi.org/10.4018/978-1-4666-0188-8.ch009

Chicago

Hartmann, Silvana, György Szarvas, and Iryna Gurevych. "Mining Multiword Terms from Wikipedia." In Semi-Automatic Ontology Development: Processes and Resources, edited by Maria Teresa Pazienza and Armando Stellato, 226-258. Hershey, PA: IGI Global, 2012. https://doi.org/10.4018/978-1-4666-0188-8.ch009

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Abstract

The collection of the specialized vocabulary of a particular domain (terminology) is an important initial step of creating formalized domain knowledge representations (ontologies). Terminology Extraction (TE) aims at automating this process by collecting the relevant domain vocabulary from existing lexical resources or collections of domain texts. In this chapter, the authors address the extraction of multiword terminology, as multiword terms are very frequent in terminology but typically poorly represented in standard lexical resources. They present their method for mining multiword terminology from Wikipedia and the freely available terminology resource that they extracted using the presented method. Terminology extraction based on Wikipedia exploits the advantages of a huge multilingual, domain-transcending knowledge source and large scale structural information that can identify potential multiword units without the need for linguistic processing tools. Thus, while evaluated in English, the proposed method is basically applicable to all languages in Wikipedia.

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