Techniques for text classification: Literature review and current trends

Automated classification of text into predefined categories has always been considered as a vital method to manage and process a vast amount of documents in digital forms that are widespread and continuously increasing. This kind of web information, popularly known as the digital/electronic information is in the form of documents, conference material, publications, journals, editorials, web pages, e-mail etc. People largely access information from these online sources rather than being limited to archaic paper sources like books, magazines, newspapers etc. But the main problem is that this enormous information lacks organization which makes it difficult to manage. Text classification is recognized as one of the key techniques used for organizing such kind of digital data. In this paper we have studied the existing work in the area of text classification which will allow us to have a fair evaluation of the progress made in this field till date. We have investigated the papers to the best of our knowledge and have tried to summarize all existing information in a comprehensive and succinct manner. The studies have been summarized in a tabular form according to the publication year considering numerous key perspectives. The main emphasis is laid on various steps involved in text classification process viz. document representation methods, feature selection methods, data mining methods and the evaluation technique used by each study to carry out the results on a particular dataset

Automatic indexing : an approach using an index term corpus and combining linguistic and statistical methods

This thesis discusses the problems and the methods of finding relevant information in large collections of documents. The contribution of this thesis to this problem is to develop better content analysis methods which can be used to describe document content with index terms. Index terms can be used as meta-information that describes documents, and that is used for seeking information. The main point of this thesis is to illustrate the process of developing an automatic indexer which analyses the content of documents by combining evidence from word frequencies and evidence from linguistic analysis provided by a syntactic parser. The indexer weights the expressions of a text according to their estimated importance for describing the content of a given document on the basis of the content analysis. The typical linguistic features of index terms were explored using a linguistically analysed text collection where the index terms are manually marked up. This text collection is referred to as an index term corpus. Specific features of the index terms provided the basis for a linguistic term-weighting scheme, which was then combined with a frequency-based term-weighting scheme. The use of an index term corpus like this as training material is a new method of developing an automatic indexer. The results of the experiments were promising