Multimedia documents place new requirements on the conventional text retrieval systems. This paper presents a multimedia retrieval system that employs the contentbased strategy to retrieve both text and speech documents. Its input can be a sequence of spoken words which are digitized waveforms or a sequence of characters, and its output is a list of ranked text and/or speech documents. In this ...

Multimedia documents place new requirements on the conventional text retrieval systems. This paper presents a multimedia retrieval system that employs the content-based strategy to retrieve both text and speech documents. Its input can be a sequence of spoken words which are digitized waveforms or a sequence of characters, and its output is a list of ranked text and/or speech documents. In this...

Text classification is one of the important research issues in the field of text mining, where the documents are classified with supervised knowledge. In literature we can find many text representation schemes and classifiers/learning algorithms used to classify text documents to the predefined categories. In this paper, we present various text representation schemes and compare different class...

Related Work in Text Similarity Recognition: Existing text similarity detection systems recognize verbatim similarities between documents but do not pay attention to similarity in expression. SCAM [4, 5], developed in the Stanford Digital Library looks for verbatim copies of text documents by fingerprinting documents and checking these fingerprints against a repository of previously known finge...

Text classification is one of the important research issues in the field of text mining, where the documents are classified with supervised knowledge. In literature we can find many text representation schemes and classifiers/learning algorithms used to classify text documents to the predefined categories. In this paper, we present various text representation schemes and compare different class...

* Faculty of Information Technology, Rangsit University. Abstract With the rapidly increasing number of Thai text documents available in digital media and websites, it is important to find an efficient text indexing technique to facilitate search and retrieval. An efficient index would speed up the response time and improve the accessibility of the documents. Up to now, not much research in Tha...

Commercial, non-profit and public organizations are accumulating huge amounts of electronically available text documents. Although composed of unstructured texts, documents contained in archives such as annual reports to shareholders, medical patient records and public announcements often share an inherent, though undocumented structure. In order to enable information integration of text collec...

Discriminating between text articles and automatically classifying documents is an essential task for many applications. With the prevalence of digital documents and the wide use of e-mail and web documents, text categorization is regaining interest and is becoming a central problem in digital text collections. There have been many approaches to solve this problem, mainly from the machine learn...

Most of the text classification problems are associated with multiple class labels and hence automatic text classification is one of the most challenging and prominent research area. Text classification is the problem of categorizing text documents into different classes. In the multi-label classification scenario, each document is associated may have more than one label. The real challenge in ...