Abstract
The availability of annotated corpora for research in the area of Readability Assessment is still very limited. On the other hand, the Web is increasingly being used by researchers as a source of written content to build very large and rich corpora, in the Web as Corpus (WaC) initiative. This paper proposes a framework for automatic generation of large corpora classified by readability. It adopts a supervised learning method to incorporate a readability filter based in features with low computational cost to a crawler, to collect texts targeted at a specific reading level. We evaluate this framework by comparing a readability-assessed web crawled corpus to a reference corpus (Both corpora are available in http://www.inf.ufrgs.br/pln/resource/CrawlingByReadabilityLevel.zip.). The results obtained indicate that these features are good at separating texts from level 1 (initial grades) from other levels. As a result of this work two Portuguese corpora were constructed: the Wikilivros Readability Corpus, classified by grade level, and a crawled WaC classified by readability level.
Keywords
- Readability assessment
- Web as a corpus
- Focused crawling
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Notes
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The toolkit is divided in a web crawling module, several combinable filter modules, a deduplication module and a post-processing module responsible for the annotation and compilation of the corpus.
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All correlations presented a significance level higher than 99 %.
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Acknowledgments
This research was partially developed in the context of the project Text Simplification of Complex Expressions, sponsored by Samsung Eletrônica da Amazônia Ltda., in the terms of the Brazilian law n. 8.248/91. This work was also partly supported by CNPq (482520/2012- 4, 312114/2015-0) and FAPERGS AiMWEst.
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Filho, J.A.W., Wilkens, R., Zilio, L., Idiart, M., Villavicencio, A. (2016). Crawling by Readability Level. In: Silva, J., Ribeiro, R., Quaresma, P., Adami, A., Branco, A. (eds) Computational Processing of the Portuguese Language. PROPOR 2016. Lecture Notes in Computer Science(), vol 9727. Springer, Cham. https://doi.org/10.1007/978-3-319-41552-9_31
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