Automatic Comparable Web Corpora Collection and Bilingual Terminology Extraction for Specialized Dictionary Making

Gurrutxaga, Antton; Leturia, Igor; Saralegi, Xabier; Vicente, Iñaki San

doi:10.1007/978-3-642-20128-8_3

Cited by 1 publication

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“…The other strategy, manual validation, means manually validating the top-n candidate terms. This is usually done by either a domain-expert or a terminologist (Chen and Yan 2017;Gurrutxaga et al 2013;Drouin 2003;Haque et al 2018;Frantzi and Ananiadou 1999). The obvious drawback of this method is that it only evaluates precision, not recall.…”

Section: Evaluation Of Monolingual Atementioning

confidence: 99%

In no uncertain terms: a dataset for monolingual and multilingual automatic term extraction from comparable corpora

Terryn

Hoste

Lefever

2019

Lang Resources & Evaluation

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Automatic term extraction is a productive field of research within natural language processing, but it still faces significant obstacles regarding datasets and evaluation, which require manual term annotation. This is an arduous task, made even more difficult by the lack of a clear distinction between terms and general language, which results in low inter-annotator agreement. There is a large need for well-documented, manually validated datasets, especially in the rising field of multilingual term extraction from comparable corpora, which presents a unique new set of challenges. In this paper, a new approach is presented for both monolingual and multilingual term annotation in comparable corpora. The detailed guidelines with different term labels, the domain-and language-independent methodology and the large volumes annotated in three different languages and four different domains make this a rich resource. The resulting datasets are not just suited for evaluation purposes but can also serve as a general source of information about terms and even as training data for supervised methods. Moreover, the gold standard for multilingual term extraction from comparable corpora contains information about term variants and translation equivalents, which allows an in-depth, nuanced evaluation.

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Section: Evaluation Of Monolingual Atementioning

confidence: 99%