Tanveer J. Siddiqui

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In this paper, a new robust steganography algorithm based on discrete cosine transform (DCT), Arnold transform and chaotic system is proposed. The chaotic system is used to generate a random sequence to be used for spreading data in the middle frequency band DCT coefficient of the cover image. The security is further enhanced by scrambling the secret data(More)
Stemmers are used to convert inflected words into their root or stem. Stem does not necessarily correspond to linguistic root of a word. Stemming improve performance by reducing morphologically variants into same words. This paper presents an approach is to develop unsupervised Hindi stemmer. This paper focus on the development of an unsupervised stemmer(More)
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— Suffix stripping is a pre-processing step required in a number of natural language processing applications. Stemmer is a tool used to perform this step. This paper presents and evaluates a rule-based and an unsupervised Marathi stemmer. The rule-based stemmer uses a set of manually extracted suffix stripping rules whereas the unsupervised approach learns(More)
We present an intelligent information retrieval model based on multi-agent paradigm and conceptual graphs. The growing amount of on-line information and its dynamic nature forces us to reconsider existing passive approaches for information retrieval. Because of this ever-growing size of information sources the burden of retrieving information can not be(More)
A new method for sentiment polarity analysis is presented. The method first assigns scores to a sentence using SentiWordNet and then uses heuristics to handle context dependent sentiment expressions. Instead of using score of all synsets of a word listed in SentiWordNet we use score of synsets of the same parts of speech only. Our method shows significant(More)
This paper presents an approach to query focused multi document summarization by combining single document summary using sentence clustering. Both syntactic and semantic similarity between sentences is used for clustering. Single document summary is generated using document feature, sentence reference index feature, location feature and concept similarity(More)
This paper investigates the effects of stemming, stop word removal and size of context window on Hindi word sense disambiguation. The evaluation has been made on a manually created sense tagged corpus consisting of Hindi words (nouns). The sense definition has been obtained from Hindi WordNet, which is an important lexical resource for Hindi language(More)