AIRCC's International Journal of Computer Science and Information Technology

M. Vidhya ¹, S. Aji ¹

Affiliations
1 Department of Computer Science, University of Kerala, Thiruvananthapuram, Kerala, IN

Abstract

In this paper, we presented a method to retrieve documents with unstructured text data written in different languages. Apart from the ordinary document retrieval systems, the proposed system can also process queries with terms in more than one language. Unicode, the universally accepted encoding standard is used to present the data in a common platform while converting the text data into Vector Space Model. We got notable F measure values in the experiments irrespective of languages used in documents and queries.

Keywords

Language Independent Searching, Information Retrieval, Multilingual Searching, Unicode, QR Factorization, Vector Space Model.

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S. Aji ¹, Ramachandra Kaimal ²

Affiliations
1 Department of Computer Science, University of Kerala, Kerala, IN
2 School of Engineering, Amrita Vishwa Vidyapeetham Amritapuri Campus, Kollam, IN

Abstract

The degree of success in document summarization processes depends on the performance of the method used in identifying significant sentences in the documents. The collection of unique words characterizes the major signature of the document, and forms the basis for Term-Sentence-Matrix (TSM). The Positive Pointwise Mutual Information, which works well for measuring semantic similarity in the Term-Sentence-Matrix, is used in our method to assign weights for each entry in the Term-Sentence-Matrix. The Sentence-Rank-Matrix generated from this weighted TSM, is then used to extract a summary from the document. Our experiments show that such a method would outperform most of the existing methods in producing summaries from large documents.

Keywords

Data Mining, Text Mining, Document Summarization, Positive Pointwise Mutual Information, Term-Sentence-Matrix.

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