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

Authors: 

Behrang QasemiZadeh, Siegfried Handschuh

Publication Type: 
Refereed Conference Meeting Proceeding
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. The dataset can be downloaded and explored from http://atmykitchen.info/datasets/acl_rd_tec/.
Conference Name: 
COLING 2014
Proceedings: 
Proceedings of the 4th International Workshop on Computational Terminology (Computerm)
Digital Object Identifer (DOI): 
978-1-873769-34-8
Publication Date: 
11/08/2014
Pages: 
52--63
Conference Location: 
Ireland
Research Group: 
Institution: 
NUIG
Open access repository: 
Yes
Publication document: