Tanveer J. Siddiqui

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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)
— 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)
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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)
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)