Search (2 results, page 1 of 1)

  • × author_ss:"Bi, Y."
  1. Wu, S.; Li, J.; Zeng, X.; Bi, Y.: Adaptive data fusion methods in information retrieval (2014) 0.01 
    0.008023808 = product of:
  0.024071421 = sum of:
    0.024071421 = product of:
      0.07221426 = sum of:
        0.07221426 = weight(_text_:retrieval in 1500) [ClassicSimilarity], result of:
          0.07221426 = score(doc=1500,freq=8.0), product of:
            0.15433937 = queryWeight, product of:
              3.024915 = idf(docFreq=5836, maxDocs=44218)
              0.051022716 = queryNorm
            0.46789268 = fieldWeight in 1500, product of:
              2.828427 = tf(freq=8.0), with freq of:
                8.0 = termFreq=8.0
              3.024915 = idf(docFreq=5836, maxDocs=44218)
              0.0546875 = fieldNorm(doc=1500)
      0.33333334 = coord(1/3)
  0.33333334 = coord(1/3)
    Abstract
    Data fusion is currently used extensively in information retrieval for various tasks. It has proved to be a useful technology because it is able to improve retrieval performance frequently. However, in almost all prior research in data fusion, static search environments have been used, and dynamic search environments have generally not been considered. In this article, we investigate adaptive data fusion methods that can change their behavior when the search environment changes. Three adaptive data fusion methods are proposed and investigated. To test these proposed methods properly, we generate a benchmark from a historic Text REtrieval Conference data set. Experiments with the benchmark show that 2 of the proposed methods are good and may potentially be used in practice.
  2. Bi, Y.: Sentiment classification in social media data by combining triplet belief functions (2022) 0.00 
    0.0034615172 = product of:
  0.010384551 = sum of:
    0.010384551 = product of:
      0.031153653 = sum of:
        0.031153653 = weight(_text_:online in 613) [ClassicSimilarity], result of:
          0.031153653 = score(doc=613,freq=2.0), product of:
            0.1548489 = queryWeight, product of:
              3.0349014 = idf(docFreq=5778, maxDocs=44218)
              0.051022716 = queryNorm
            0.20118743 = fieldWeight in 613, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.0349014 = idf(docFreq=5778, maxDocs=44218)
              0.046875 = fieldNorm(doc=613)
      0.33333334 = coord(1/3)
  0.33333334 = coord(1/3)
    Abstract
    Sentiment analysis is an emerging technique that caters for semantic orientation and opinion mining. It is increasingly used to analyze online reviews and posts for identifying people's opinions and attitudes to products and events in order to improve business performance of companies and aid to make better organizing strategies of events. This paper presents an innovative approach to combining the outputs of sentiment classifiers under the framework of belief functions. It consists of the formulation of sentiment classifier outputs in the triplet evidence structure and the development of general formulas for combining triplet functions derived from sentiment classification results via three evidential combination rules along with comparative analyses. The empirical studies have been conducted on examining the effectiveness of our method for sentiment classification individually and in combination, and the results demonstrate that the best combined classifiers by our method outperforms the best individual classifiers over five review datasets.

Authors

Years