The need for an efficient method to find the furthermost appropriate document corresponding a particular search query has become crucial due exponential development in number of papers that are now readily available us on web. vector space model (VSM) perfect used “information retrieval”, represents these words as and gives them weights via popular weighting known term frequency inverse (TF-IDF...

This paper describes a text mining tool that performs two tasks, namely document clustering and text summarization. These tasks have, of course, their corresponding counterpart in “conventional” data mining. However, the textual, unstructured nature of documents makes these two text mining tasks considerably more difficult than their data mining counterparts. In our system document clustering i...

This work proposes a new extractive text-summarization algorithm based on the importance of the topics contained in a document. The basic ideas of the proposed algorithm are as follows. At first the document is partitioned by using the TextTiling algorithm, which identifies topics (coherent segments of text) based on the TF-IDF metric. Then for each topic the algorithm computes a measure of its...

In this paper we combine our previous research in the field of Semantic web, especially ontology learning and population with Sentence retrieval. To do this we developed a new approach to sentence retrieval modifying our previous TF-ISF method which uses local context information to take into account only document level information. This is quite a new approach to sentence retrieval, presented ...

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We describe SiSPI, a clustering tool based on an unsupervised and incremental approach which aims at arranging short passages from one or multiple documents written in Brazilian Portuguese into clusters. In order to identify similar passages, SiSPI makes use of a statistical model, named TF-ISF (Term Frequency Inverse Sentence Frequency). By grouping similar passages into the same cluster, SiSP...