2026-05-01T00:27:30Z https://meral.edu.mm/oai

oai:meral.edu.mm:recid/3629 2021-12-13T03:38:49Z 1582963302567:1597824273898 user-ucsy

Classifying Textual Documents Using Two Dimensional Probabilistic Model Than, Wai Me Me Kham, Nang Saing Moon This paper presents the probabilistic model named TwodimensionalProbabilistic Model (2DPM). In this model, terms are seen as disjoinevents, and terms and categories are realeated to each other. Since the documentsare represented as the union of terms, disjoint event, document and categories arealso rreleated. Terms are measured with their presence and expressiveness. Thepresentce and expressivencess of a term is defined as the peculiarity of that term. Adocument is defined as set of terms and it also has presence and expressivenessfor a category. So, the 2DPM model defines a direct relationship between theprobability of a document given a category of interest and a point on atwodimensionalspace. With the points, entire collections of documents are graphed ona Cartesian plane and documents are classifie directly on the two-dimensionalrepresentation. To experiment the system, Reuters-21578 newswire dataset is usedfor text classification. 2011-12-29 http://hdl.handle.net/20.500.12678/0000003629 https://meral.edu.mm/records/3629