Detection of Opinions and Facts. A Cognitive Approach
نویسندگان
چکیده
A model of episodic memory is derived to propose algorithms of text categorization with semantic space models. Performances of two algorithms named Target vector and Sub-target vector are contrasted using textual material of the text-mining context ‘DEFT09’. The experience reported here have been realized on the english corpus which is composed of articles of the economic newspaper “The Financial Times”. The aim of the task was to categorize texts in function of the factuality or subjectivity they expressed. Results confirm (i) that the episodic memory metaphor provides a convenient framework to propose efficient algorithm for text categorization, and (ii) that Sub-target vector algorithm outperforms the Target vector algorithm.
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