Abstract
Retrieving information that is implicit in a text is difficult. For argument analysis, revealing implied knowledge could be useful to judge how solid an argument is and to construct concise arguments. We design a process for obtaining high-quality implied knowledge annotations for German argumentative microtexts, in the form of simple natural language statements. This process involves several steps to promote agreement and monitors its evolution using textual similarity computation. To further characterize the implied knowledge, we annotate the added sentences with semantic clause types and common sense knowledge relations. To test whether the knowledge could be retrieved automatically, we compare the inserted sentences to Wikipedia articles on similar topics. Analysis of the added knowledge shows that (i) it is characterized by a high proportion of generic sentences, (ii) a majority of it can be mapped to common sense knowledge relations, and (iii) it is similar to sentences found in Wikipedia.
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Notes
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All examples are shown in English for convenience.
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Spearman correlation results on the German version of the MC30: 0.76; RG65: 0.79; wordsim353: 0.69; ZG222: 0.42. https://dkpro.github.io/dkpro-similarity/wordpairsimilarity/.
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Acknowledgments
This work has been conducted within the Leibniz ScienceCampus “Empirical Linguistics and Computational Modeling”, funded by the Leibniz Association under grant no. SAS-2015-IDS-LWC and by the Ministry of Science, Research and Art (MWK) of the state of Baden-Württemberg. We thank our annotators Sabrina Effenberger, Jesper Klein, Sarina Meyer and Rebekka Sons for their contribution.
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Becker, M., Staniek, M., Nastase, V., Frank, A. (2017). Enriching Argumentative Texts with Implicit Knowledge. In: Frasincar, F., Ittoo, A., Nguyen, L., Métais, E. (eds) Natural Language Processing and Information Systems. NLDB 2017. Lecture Notes in Computer Science(), vol 10260. Springer, Cham. https://doi.org/10.1007/978-3-319-59569-6_9
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DOI: https://doi.org/10.1007/978-3-319-59569-6_9
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