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  • × author_ss:"Srinivasan, P."
  • × year_i:[2000 TO 2010}
  1. Ruiz, M.E.; Srinivasan, P.: Combining machine learning and hierarchical indexing structures for text categorization (2001) 0.00 
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    Abstract
    This paper presents a method that exploits the hierarchical structure of an indexing vocabulary to guide the development and training of machine learning methods for automatic text categorization. We present the design of a hierarchical classifier based an the divide-and-conquer principle. The method is evaluated using backpropagation neural networks, such as the machine learning algorithm, that leam to assign MeSH categories to a subset of MEDLINE records. Comparisons with traditional Rocchio's algorithm adapted for text categorization, as well as flat neural network classifiers, are provided. The results indicate that the use of hierarchical structures improves Performance significantly.
    Type
    a
  2. Srinivasan, P.: Text mining in biomedicine : challenges and opportunities (2006) 0.00 
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    Abstract
    Text mining is about making serendipity more likely. Serendipity, the chance discovery of interesting ideas, has been responsible for many discoveries in science. Text mining systems strive to explore large text collections, separate the potentially meaningfull connections from a vast and mostly noisy background of random associations. In this paper we provide a summary of our text mining approach and also illustrate briefly some of the experiments we have conducted with this approach. In particular we use a profile-based text mining method. We have used these profiles to explore the global distribution of disease research, replicate discoveries made by others and propose new hypotheses. Text mining holds much potential that has yet to be tapped.
    Source
    Knowledge organization, information systems and other essays: Professor A. Neelameghan Festschrift. Ed. by K.S. Raghavan and K.N. Prasad
    Type
    a
  3. Srinivasan, P.: Text mining : generating hypotheses from MEDLINE (2004) 0.00 
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    Abstract
    Hypothesis generation, a crucial initial step for making scientific discoveries, relies an prior knowledge, experience, and intuition. Chance connections made between seemingly distinct subareas sometimes turn out to be fruitful. The goal in text mining is to assist in this process by automatically discovering a small set of interesting hypotheses from a suitable text collection. In this report, we present open and closed text mining algorithms that are built within the discovery framework established by Swanson and Smalheiser. Our algorithms represent topics using metadata profiles. When applied to MEDLINE, these are McSH based profiles. We present experiments that demonstrate the effectiveness of our algorithms. Specifically, our algorithms successfully generate ranked term lists where the key terms representing novel relationships between topics are ranked high.
    Type
    a

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