Calculus of Fuzzy Semantic Typing for Qualitative Analysis of Text
نویسندگان
چکیده
Statistical approaches to text mining can be enhanced and improved through the qualitative representation of free text – ideally, a representation which accommodates ambiguity and imprecision. We introduce a specialized lexicon that assigns semantic categories to words, together with numeric values for centrality and intensity within each category. From this lexicon, we automatically generate an additional set of resources to implement some of the common operations of text mining – profiling, querying, and query/profile expansion and compression – in qualitative domains. We exploit the hierarchical structure of free text (i.e., sentence/ paragraph/ document) and develop a set of operators whose arguments are fuzzy representations ("profiles") of text at any hierarchical level. Various operators compute the centrality and intensity of categories within a profile, a profile's overall intensity, and the cardinality and fuzziness of a profile; others are used in profile merging, profile expansion or compression, and discovery of related categories from a profile. We address the meaning and modes of deployment of these operators using practical examples. Finally, we discuss the utility of fuzzy typing for various tasks, such as "qualitative browsing" and similarity estimates. We discuss how the existing approach can be enhanced using automatic lexicon expansion and information extraction techniques. We offer a practical software demonstration with several visualization examples, illustrating the power of the proposed operators in affect analysis of news reports and movie reviews.
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