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Some examples of document similarity are document clustering, document categorization, document summarization, and query-based search.
Such analysis is facilitated through use of text mining techniques, ranging from simple statistical pattern learning based on term frequencies, to complex natural language processing techniques in order to produce text categorization, document summarization, information retrieval, and ultimately the data mining [ 6].
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In standard data mining approach to text categorization, documents represent as bag-of-word vectors.
The categorization of document types and coupling to organizational roles yields the opportunity for the systematic and meticulous definition of interfaces.
The methods under consideration are designed for categorization of documents consisting of a collection of fields, or arbitrary tree-structured documents that can be adequately modeled with such a flat structure.
The detailed categorization of documents revealed that semi-structured reports with an uncommon layout were processed less accurate but also fairly well (micro avg. f1=.915).
HM has been successfully used in many structured pattern recognition problems, such as document categorization [ 5], web contend classification [ 15] and so on.
PKIP tool has an advantage over the traditional document categorization methods in that it can correctly categorize the question item which lacks keywords since it adopts the feature selection technique and support vector machine approach to item bank text categorization.
This paper examines several different approaches to exploiting structural information in semi-structured document categorization.
Document categorization is one of the foundational problems in (web) information retrieval.
All of which can be used for setting up more efficient search strategies and facilitated document categorization.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com