Tailoring and evaluating the Wikipedia for in-domain comparable corpora extraction

Author:

España-Bonet CristinaORCID,Barrón-Cedeño AlbertoORCID,Màrquez Lluís

Abstract

AbstractWe propose a language-independent graph-based method to build à-la-carte article collections on user-defined domains from the Wikipedia. The core model is based on the exploration of the encyclopedia’s category graph and can produce both mono- and multilingual comparable collections. We run thorough experiments to assess the quality of the obtained corpora in 10 languages and 743 domains. According to an extensive manual evaluation, our graph model reaches an average precision of $$84\%$$ 84 % on in-domain articles, outperforming an alternative model based on information retrieval techniques. As manual evaluations are costly, we introduce the concept of domainness and design several automatic metrics to account for the quality of the collections. Our best metric for domainness shows a strong correlation with human judgments, representing a reasonable automatic alternative to assess the quality of domain-specific corpora. We release the toolkit with the implementation of the extraction methods, the evaluation measures and several utilities.

Funder

Ministerio de Economía y Competitividad

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Hardware and Architecture,Human-Computer Interaction,Information Systems,Software

Reference58 articles.

1. Adafre S, de Rijke M (2006) Finding Similar Sentences across Multiple Languages in Wikipedia, In: Proceedings of the 11th conference of the European chapter of the association for computational linguistics (EACL), pp 62–69

2. Aker A, Kanoulas E, Gaizauskas R (2012) A light way to collect comparable corpora from the Web, In: Calzolari N, Choukri K, Declerck T, Dogan M, Maegaard B, Mariani J, Odijk J and Piperidis S (eds) Proceedings of the eighth international conference on language resources and evaluation (LREC), European Language Resources Association (ELRA), Istanbul, Turkey, pp 15–20

3. Artetxe M, Schwenk H (2019) Massively multilingual sentence embeddings for zero-shot cross-lingual transfer and beyond. Trans Assoc Comput Linguist (TACL) 7:597–610

4. Aspert N, Miz V, Ricaud B, Vandergheynst P (2019) A Graph-Structured Dataset for Wikipedia Research, In: Companion Proceedings of The 2019 World Wide Web conference (WWW), Association for Computing Machinery (ACM), New York, NY, USA, pp 1188–1193

5. Barrón-Cedeño A, España-Bonet C, Boldoba J, Màrquez L (2015) A Factory of Comparable Corpora from Wikipedia, In: Proceedings of the 8th Workshop on Building and Using Comparable Corpora (BUCC), Beijing, China, pp 3–13. http://www.aclweb.org/anthology/W15-3402

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