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dc.contributor.authorQasemiZadeh, Behrang
dc.contributor.authorHandschuh, Siegfried
dc.date.accessioned2014-06-11T11:54:42Z
dc.date.available2014-06-11T11:54:42Z
dc.date.issued2014
dc.identifier.citationBehrang QasemiZadeh and Siegfried Handschuh (2014) Evaluation of Technology Term Recognition with Random Indexing Proceedings of the Ninth International Conference on Language Resources and Evaluationen_US
dc.identifier.urihttp://www.lrec-conf.org/proceedings/lrec2014/pdf/920_Paper.pdf
dc.identifier.urihttp://hdl.handle.net/10379/4381
dc.description.abstractIn this paper, we propose a method that combines the principles of automatic term recognition and the distributional hypothesis to identify technology terms from a corpus of scientific publications. We employ the random indexing technique to model terms surrounding words, which we call the context window, in a vector space at reduced dimension. The constructed vector space and a set of reference vectors, which represents manually annotated technology terms, in a k-nearest-neighbour voting classification scheme are used for term classification. In this paper, we examine a number of parameters that influence the obtained results. First, we inspect several context configurations, i.e. the effect of the context window size, the direction in which co-occurrence counts are collected, and information about the order of words within the context windows. Second, in the k-nearest-neighbour voting scheme, we study the role that neighbourhood size selection plays, i.e. the value of k. The obtained results are similar to word space models. The performed experiments suggest the best performing context are small (i.e. not wider than 3 words), are extended in both directions and encode the word order information. Moreover, the accomplished experiments suggest that the obtained results, to a great extent, are independent of the value of k.en_US
dc.language.isoenen_US
dc.relation.ispartofProceedings of the Ninth International Conference on Language Resources and Evaluationen
dc.subjectRandom Indexing, Distributional Semantics, Text Mining, Terminology Extractionen_US
dc.titleEvaluation of Technology Term Recognition with Random Indexingen_US
dc.typeConference Paperen_US
dc.date.updated2014-06-05T14:02:55Z
dc.identifier.doihttp://www.lrec-conf.org/proceedings/lrec2014/pdf/920_Paper.pdf
dc.description.peer-reviewedpeer-reviewed
dc.contributor.funder|~|SFI|~|
dc.internal.rssid6464065
dc.local.contactBehrang Qasemizadeh, Deri, Ida Business Park, Lower Dangan, Nui Galway. Email: behrang.qasemizadeh@deri.org
dc.local.copyrightcheckedYes
dc.local.versionACCEPTED
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