With the growth of internet, the amount of digital information is growing exponentially day by day. This information may be structured or unstructured in nature. So, a need to convert unstructured text into structured text and to infer knowledge was felt As a result of this, the field of text mining emerged. Text documents may be in the form of online news articles, emails, scientific documents...

This thesis is about multilingual document clustering through estimating semantic relatedness between multilingual texts. Specifically we focus on the task of clustering multilingual documents with very limited or no supervisory information. We present two approaches to address the problem : a comparable-corpora based approach and a web-searches based approach. Our first approach derives pairwi...

This work provides algorithms and heuristics to index text documents by determining important topics in the documents. To index text documents, the work provides algorithms to generate topic candidates, determine their importance, detect similar and synonym topics, and to eliminate incoherent topics. The indexing algorithm uses topic frequency to determine the importance and the existence of th...

Clustering text documents into different category groups is an important step in indexing, retrieval, management and mining of abundant text data on the Web or in corporate information systems. Text clustering task can be intuitively described as finding, given a set vectors of some data points in a multi-dimensional space, a partition of text data into clusters such that the points within each...

OBJECTIVES With the exponential increase in the number of articles published every year in the biomedical domain, there is a need to build automated systems to extract unknown information from the articles published. Text mining techniques enable the extraction of unknown knowledge from unstructured documents. METHODS This paper reviews text mining processes in detail and the software tools a...

Text reuse is common in many scenarios and documents are often based, at least in part, on existing documents. This paper reports an approach to detecting text reuse which identifies not only documents which have been reused verbatim but is also designed to identify cases of reuse when the original has been rewritten. The approach identifies reuse by comparing word n-grams in documents and modi...

This paper proposes an ad hoc retrieval method for spoken documents that uses a statistical translation technique. After transcribing the spoken documents by using a Large-Vocabulary Continuous Speech Recognition (LVCSR) decoder, a text-based ad hoc retrieval method can be directly applied to the transcribed documents. However, recognition errors will signi cantly degrade the retrieval performa...

Text Classification, also known as text categorization, is the task of automatically allocating unlabeled documents into predefined categories. Text Classification means allocating a document to one or more categories or classes. The ability to accurately perform a classification task depends on the representations of documents to be classified. Text representations transform the textural docum...

Text mining has been gaining popularity in the knowledge discovery field, particularity with the increasing availability of digital documents in various languages from all around the world. However, currently most text mining tools mainly focus on processing monolingual documents (particularly English documents) only, little attention has been paid to apply the techniques to handle the document...