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  1. The Text Mining Handbook
  2. Interdependence of Text Mining Quality and the Input Data Preprocessing
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  4. The Text Mining Handbook: Advanced Approaches in Analyzing Unstructured Data | BibSonomy

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Extract Structured Data from unstructured Text (Text Mining Using R)

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Despite the somewhat misleading label that it bears as unstructured data , a text document may be seen, from many perspectives, as a structured object.

From a linguistic perspective, even a rather innocuous document demonstrates a rich amount of semantic and syntactical structure, although this structure is implicit and to some degree hidden in its textual content. Word sequence may also be a structurally meaningful dimension to a document. Documents that have relatively little in the way of strong typographical, layout, or markup indicators to denote structure — like most scientific research papers, business reports, legal memoranda, and news stories — are sometimes referred to as free-format or weakly structured documents.

On the other hand, documents with extensive and consistent format elements in which field-type metadata can be more easily inferred — such as some e-mail, HTML Web pages, PDF files, and word-processing files with heavy document templating or style-sheet constraints — are occasionally described as semistructured documents. The preprocessing operations that support text mining attempt to leverage many different elements contained in a natural language document in order to transform it from an irregular and implicitly structured representation into an explicitly structured representation.

However, given the potentially large number of words, phrases, sentences, typographical elements, and layout artifacts that even a short document may have — not to mention the potentially vast number of different senses that each of these elements may have in various contexts and combinations — an essential task for most text mining systems is the identification of a simplified subset of document features that can be used to represent a particular document as a whole.

We refer to such a set of features as the representational model of a document and say that individual documents are represented by the set of features that their representational models contain. Even with attempts to develop efficient representational models, each document in a collection is usually made up of a large number — sometimes an exceedingly large number — of features.


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Problems relating to high featuredimensionality i. Structured representations of natural language documents have much larger numbers of potentially representative features — and thus higher numbers of possible combinations of feature values — than one generally finds with records in relational or hierarchical databases. For even the most modest document collections, the number of word-level features required to represent the documents in these collections can be exceedingly large.

For example, in an extremely small collection of 15, documents culled from Reuters news feeds, more than 25, nontrivial word stems could be identified.