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Journal of Intelligent Information Systems

Erschienen in:

18.03.2016

Multilingual news extraction via stopword language model scoring

verfasst von: Yu-Chieh Wu

Erschienen in: Journal of Intelligent Information Systems | Ausgabe 1/2017

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Abstract

Web news provides a quick and convenient means to create collections of large documents. The creation of a web news corpus has typically required the construction of a set of HTML parsing rules to identify content text. In general, these parsing rules are written manually and treat different web pages differently. We address this issue and propose a news content recognition algorithm that is language and layout independent. Our method first scans a given HTML document and roughly localizes a set of candidate news areas. Next, we apply a designed scoring function to rank the best content. To validate this approach, we evaluate the systems performance using 1092 items of multilingual web news data covering 17 global regions and 11 distinct languages. We compare these data with nine published content extraction systems using standard settings. The results of this empirical study show that our method outperforms the second-best approach (Boilerpipe) by 6.04 and 10.79 % with regard to the relative micro and macro F-measures, respectively. We also apply our system to monitor online RSS news distribution. It collected 0.4 million news articles from 200 RSS channels in 20 days. This sample quality test shows that our method achieved 93 % extraction accuracy for large news streams.

Vorheriger Artikel TasteMiner: Mining partial tastes for neighbor-based collaborative filtering

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Titel: Multilingual news extraction via stopword language model scoring
verfasst von: Yu-Chieh Wu
Publikationsdatum: 18.03.2016
Verlag: Springer US
Erschienen in: Journal of Intelligent Information Systems / Ausgabe 1/2017
Print ISSN: 0925-9902
Elektronische ISSN: 1573-7675
DOI: https://doi.org/10.1007/s10844-016-0395-6

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