10.1.1.153.6679 - Andreas Hotho Andreas Nrnberger and...

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Andreas Hotho, Andreas Nürnberger, and Gerhard Paaß A Brief Survey of Text Mining The enormous amount of information stored in unstructured texts can- not simply be used for further processing by computers, which typically handle text as simple sequences of character strings. Therefore, specific (pre-)processing methods and algorithms are required in order to extract useful patterns. Text mining refers generally to the process of extracting interesting information and knowledge from unstructured text. In this ar- ticle, we discuss text mining as a young and interdisciplinary field in the intersection of the related areas information retrieval, machine learning, statistics, computational linguistics and especially data mining. We describe the main analysis tasks preprocessing, classification, clustering, information extraction and visualization. In addition, we briefly discuss a number of successful applications of text mining. 1 Introduction As computer networks become the backbones of science and economy enor- mous quantities of machine readable documents become available. There are estimates that 85 % of business information lives in the form of text (TMS 05 2005 ). Unfortunately, the usual logic-based programming paradigm has great difficulties in capturing the fuzzy and often ambiguous relations in text doc- uments. Text mining aims at disclosing the concealed information by means of methods which on the one hand are able to cope with the large number of words and structures in natural language and on the other hand allow to handle vagueness, uncertainty and fuzziness. In this paper we describe text mining as a truly interdisciplinary method drawing on information retrieval, machine learning, statistics, computational linguistics and especially data mining. We first give a short sketch of these meth- ods and then define text mining in relation to them. Later sections survey state of the art approaches for the main analysis tasks preprocessing, classification, clustering, information extraction and visualization. The last section exemplifies text mining in the context of a number of successful applications. LDV FORUM – Band 20 – 2005 19
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Hotho, Nürnberger, and Paaß 1.1 Knowledge Discovery In literature we can find different definitions of the terms knowledge discovery or knowledge discovery in databases (KDD) and data mining. In order to distinguish data mining from KDD we define KDD according to Fayyad as follows (Fayyad et al. 1996 ): Knowledge Discovery in Databases (KDD) is the non-trivial pro- cess of identifying valid, novel, potentially useful, and ultimately understandable patterns in data. The analysis of data in KDD aims at finding hidden patterns and connections in these data. By data we understand a quantity of facts, which can be, for instance, data in a database, but also data in a simple text file. Characteristics that can be used to measure the quality of the patterns found in the data are the comprehensibility for humans, validity in the context of given statistic measures, novelty and usefulness. Furthermore, different methods are able to discover not
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