Search (3 results, page 1 of 1)

  • × author_ss:"Ghosh, S."
  1. Sen, S.; Biswas, A.; Ghosh, S.: Adaptive choice of information sources (1999) 0.00 
    0.0029294936 = product of:
  0.005858987 = sum of:
    0.005858987 = product of:
      0.011717974 = sum of:
        0.011717974 = weight(_text_:a in 3982) [ClassicSimilarity], result of:
          0.011717974 = score(doc=3982,freq=6.0), product of:
            0.053105544 = queryWeight, product of:
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.046056706 = queryNorm
            0.22065444 = fieldWeight in 3982, product of:
              2.4494898 = tf(freq=6.0), with freq of:
                6.0 = termFreq=6.0
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.078125 = fieldNorm(doc=3982)
      0.5 = coord(1/2)
  0.5 = coord(1/2)
    Abstract
    We present a number of learning approaches by which agents can adapt to select information sources that satisfy performance requirements
    Type
    a
  2. Sankarasubramaniam, Y.; Ramanathan, K.; Ghosh, S.: Text summarization using Wikipedia (2014) 0.00 
    0.0020714647 = product of:
  0.0041429293 = sum of:
    0.0041429293 = product of:
      0.008285859 = sum of:
        0.008285859 = weight(_text_:a in 2693) [ClassicSimilarity], result of:
          0.008285859 = score(doc=2693,freq=12.0), product of:
            0.053105544 = queryWeight, product of:
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.046056706 = queryNorm
            0.15602624 = fieldWeight in 2693, product of:
              3.4641016 = tf(freq=12.0), with freq of:
                12.0 = termFreq=12.0
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.0390625 = fieldNorm(doc=2693)
      0.5 = coord(1/2)
  0.5 = coord(1/2)
    Abstract
    Automatic text summarization has been an active field of research for many years. Several approaches have been proposed, ranging from simple position and word-frequency methods, to learning and graph based algorithms. The advent of human-generated knowledge bases like Wikipedia offer a further possibility in text summarization - they can be used to understand the input text in terms of salient concepts from the knowledge base. In this paper, we study a novel approach that leverages Wikipedia in conjunction with graph-based ranking. Our approach is to first construct a bipartite sentence-concept graph, and then rank the input sentences using iterative updates on this graph. We consider several models for the bipartite graph, and derive convergence properties under each model. Then, we take up personalized and query-focused summarization, where the sentence ranks additionally depend on user interests and queries, respectively. Finally, we present a Wikipedia-based multi-document summarization algorithm. An important feature of the proposed algorithms is that they enable real-time incremental summarization - users can first view an initial summary, and then request additional content if interested. We evaluate the performance of our proposed summarizer using the ROUGE metric, and the results show that leveraging Wikipedia can significantly improve summary quality. We also present results from a user study, which suggests that using incremental summarization can help in better understanding news articles.
    Type
    a
  3. Ghosh, S.; Panigrahi, P.: Use of Ranganathan's analytico-synthetic approach in developing a domain ontology in library and information science (2015) 0.00 
    0.0018909799 = product of:
  0.0037819599 = sum of:
    0.0037819599 = product of:
      0.0075639198 = sum of:
        0.0075639198 = weight(_text_:a in 2798) [ClassicSimilarity], result of:
          0.0075639198 = score(doc=2798,freq=10.0), product of:
            0.053105544 = queryWeight, product of:
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.046056706 = queryNorm
            0.14243183 = fieldWeight in 2798, product of:
              3.1622777 = tf(freq=10.0), with freq of:
                10.0 = termFreq=10.0
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.0390625 = fieldNorm(doc=2798)
      0.5 = coord(1/2)
  0.5 = coord(1/2)
    Abstract
    Classification is the basis of knowledge organization. Ontology, a comparatively new concept used as a tool for knowledge organization, establishes connections between terms and concepts enhancing the scope and usefulness of library classification. Ranganathan had invented the strong theory of the analytico-synthetic method in classification and devised Colon Classification. In this study a domain ontology on library and information science has been developed by implementing Raganathan's faceted approach of classification. The hierarchical relationships among terms have been established primarily keeping conformity with that of Ranganathan's Colon Classification (7th edition). But to accommodate new vocabularies, DDC 23rd edition and UDC Standard edition are consulted. The Protégé ontology editor has been used. The study carefully examines the steps in which the analytico-synthetic method have been followed. Ranganathan's Canon of Characteristics and its relevant Canons have been followed for defining the class-subclass hierarchy. It concludes by identifying the drawbacks as well as the merits faced while developing the ontology. This paper proves the relevance and importance of Ranganathan's philosophy in developing ontology based knowledge organization.
    Type
    a

Authors

Years

Themes