In this paper we investigate the problem of identifying the perspective from which a document was written. By perspective we mean a point of view, for example, from the perspective of Democrats or Republicans. Can computers learn to identify the perspective of a document? Furthermore, can computers identify which sentences in a document strongly convey a particular perspective? We develop statistical models to capture how perspectives are expressed at the document and sentence levels, and evaluate the proposed models on a collection of articles on the Israeli-Palestinian conflict. The results show that the statistical models can successfully learn how perspectives are reflected in word usage and identify the perspective of a document with very high accuracy.
CITATION STYLE
Lin, W. H. (2006). Identifying perspectives at the document and sentence levels using statistical models. In HLT-NAACL 2006 - Human Language Technology Conference of the North American Chapter of the Association of Computational Linguistics, Proceedings of the Doctoral Consortium (pp. 227–230). Association for Computational Linguistics (ACL). https://doi.org/10.3115/1225797.1225802
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