Abstract
Nowadays, very often decision making relies on information that is found in the various Internet sources. Preferred are texts of the encyclopedic style, which contain mostly factual information. We propose to combine the logic-linguistic model and the universal dependency treebank to extract facts of various quality levels from texts. Based on Random Forest as a classification algorithm, we show the most significant types of facts and types of words that most affect the encyclopedic-style of the text. We evaluate our approach on four corpora based on Wikipedia, social and mass media texts. Our classifier achieves over 90% F-measure.
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Notes
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We use ‘Predicate’, ‘Subject’ and ‘Object’ with the first upper-case letters to emphasize the semantic meaning of words in a sentence.
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Groups description available on the page http://u.cs.biu.ac.il/~koppel/BlogCorpus.htm.
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Khairova, N., Lewoniewski, W., Węcel, K., Orken, M., Kuralai, M. (2018). Comparative Analysis of the Informativeness and Encyclopedic Style of the Popular Web Information Sources. In: Abramowicz, W., Paschke, A. (eds) Business Information Systems. BIS 2018. Lecture Notes in Business Information Processing, vol 320. Springer, Cham. https://doi.org/10.1007/978-3-319-93931-5_24
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