Terms are notoriously difficult to identify, both automatically and manually. This complicates the evaluation of the already challenging task of automatic term extraction. With the advent of multilingual automatic term extraction from comparable corpora, accurate evaluation becomes increasingly difficult, since term linking must be evaluated as well as term extraction. A gold standard with manual annotations for a complete comparable corpus has been developed, based on a novel methodology created to accommodate for the intrinsic difficulties of this task. In this contribution, we show how the effort involved in the development of this gold standard resulted, not only in a tool for evaluation, but also in a rich source of information about terms. A detailed analysis of term characteristics illustrates how such knowledge about terms may inspire improvements for automatic term extraction.
@InProceedings{RIGOUTS TERRYN18.598, author = {Ayla Rigouts Terryn and Veronique Hoste and Els Lefever}, title = "{A Gold Standard for Multilingual Automatic Term Extraction from Comparable Corpora: Term Structure and Translation Equivalents}", booktitle = {Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)}, year = {2018}, month = {May 7-12, 2018}, address = {Miyazaki, Japan}, editor = {Nicoletta Calzolari (Conference chair) and Khalid Choukri and Christopher Cieri and Thierry Declerck and Sara Goggi and Koiti Hasida and Hitoshi Isahara and Bente Maegaard and Joseph Mariani and Hélène Mazo and Asuncion Moreno and Jan Odijk and Stelios Piperidis and Takenobu Tokunaga}, publisher = {European Language Resources Association (ELRA)}, isbn = {979-10-95546-00-9}, language = {english} }
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