Search (3 results, page 1 of 1)

  • × author_ss:"Srinivasan, P."
  • × theme_ss:"Data Mining"
  1. Qiu, X.Y.; Srinivasan, P.; Hu, Y.: Supervised learning models to predict firm performance with annual reports : an empirical study (2014) 0.00 
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    Abstract
    Text mining and machine learning methodologies have been applied toward knowledge discovery in several domains, such as biomedicine and business. Interestingly, in the business domain, the text mining and machine learning community has minimally explored company annual reports with their mandatory disclosures. In this study, we explore the question "How can annual reports be used to predict change in company performance from one year to the next?" from a text mining perspective. Our article contributes a systematic study of the potential of company mandatory disclosures using a computational viewpoint in the following aspects: (a) We characterize our research problem along distinct dimensions to gain a reasonably comprehensive understanding of the capacity of supervised learning methods in predicting change in company performance using annual reports, and (b) our findings from unbiased systematic experiments provide further evidence about the economic incentives faced by analysts in their stock recommendations and speculations on analysts having access to more information in producing earnings forecast.
    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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