{[ promptMessage ]}

Bookmark it

{[ promptMessage ]}

The currently predominant approaches based on this

Info iconThis preview shows page 1. Sign up to view the full content.

View Full Document Right Arrow Icon
This is the end of the preview. Sign up to access the rest of the document.

Unformatted text preview: Text Mining order to be able to define at least the importance of a word within a given document, usually a vector representation is used, where for each word a numerical "importance" value is stored. The currently predominant approaches based on this idea are the vector space model (Salton et al. 1975), the probabilistic model (Robertson 1977) and the logical model (van Rijsbergen 1986). In the following we briefly describe, how a bag-of-words representation can be obtained. Furthermore, we describe the vector space model and corresponding similarity measures in more detail, since this model will be used by several text mining approaches discussed in this article. 2.1 Text Preprocessing In order to obtain all words that are used in a given text, a tokenization process is required, i.e. a text document is split into a stream of words by removing all punctuation marks and by replacing tabs and other non-text characters by single white spaces. This tokenized representation is then used for further processing. The set of different words obtained by merging all text documents of a collection is called the dictionary of a...
View Full Document

{[ snackBarMessage ]}

Ask a homework question - tutors are online