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The ACL RD-TEC: A Dataset for Benchmarking Terminology Extraction and Classification in Computational Linguistics

QasemiZadeh, Behrang
; 
Handschuh, Siegfried
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2014
Keywords
terminology evaluation, term recognition, term classification, terminology evaluation dataset, automatic term recognition, natural language processing, computational linguistics
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Conference Paper
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QasemiZadeh, Behrang; Handschuh, Siegfried; (2014) The ACL RD-TEC: A Dataset for Benchmarking Terminology Extraction and Classification in Computational Linguistics . In: Patrick Drouin and Natalia Grabar and Thierry Hamon and Kyo Kageura eds. COLING 2014: 4th International Workshop on Computational Terminology Dublin, Ireland, 2014-08-23- 2014-08-23
Abstract
This paper introduces ACL RD-TEC: a dataset for evaluating the extraction and classification of terms from literature in the domain of computational linguistics. The dataset is derived from the Association for Computational Linguistics anthology reference corpus (ACL ARC). In its first release, the ACL RD-TEC consists of automatically segmented, part-of-speech-tagged ACL ARC documents, three lists of candidate terms, and more than 82,000 manually annotated terms. The annotated terms are marked as either valid or invalid, and valid terms are further classified as technology and non-technology terms. Technology terms signify methods, algorithms, and solutions in computational linguistics. The paper describes the dataset and reports the relevant statistics. We hope the step described in this paper encourages a collaborative effort towards building a full-fledged annotated corpus from the computational linguistics literature.
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Attribution-NonCommercial-NoDerivs 3.0 Ireland
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Data Science Institute (Conference Papers)