Search (5 results, page 1 of 1)

0.0018075579 = product of:
  0.014460463 = sum of:
    0.014460463 = product of:
      0.04338139 = sum of:
        0.04338139 = weight(_text_:problem in 2714) [ClassicSimilarity], result of:
          0.04338139 = score(doc=2714,freq=4.0), product of:
            0.13082431 = queryWeight, product of:
              4.244485 = idf(docFreq=1723, maxDocs=44218)
              0.030822188 = queryNorm
            0.33160037 = fieldWeight in 2714, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              4.244485 = idf(docFreq=1723, maxDocs=44218)
              0.0390625 = fieldNorm(doc=2714)
      0.33333334 = coord(1/3)
  0.125 = coord(1/8)

Abstract

Most existing search engines focus on document retrieval. However, information needs are certainly not limited to finding relevant documents. Instead, a user may want to find relevant entities such as persons and organizations. In this paper, we study the problem of related entity finding. Our goal is to rank entities based on their relevance to a structured query, which specifies an input entity, the type of related entities and the relation between the input and related entities. We first discuss a general probabilistic framework, derive six possible retrieval models to rank the related entities, and then compare these models both analytically and empirically. To further improve performance, we study the problem of feedback in the context of related entity finding. Specifically, we propose a mixture model based feedback method that can utilize the pseudo feedback entities to estimate an enriched model for the relation between the input and related entities. Experimental results over two standard TREC collections show that the derived relation generation model combined with a relation feedback method performs better than other models.

0.0012781365 = product of:
  0.010225092 = sum of:
    0.010225092 = product of:
      0.030675275 = sum of:
        0.030675275 = weight(_text_:problem in 2210) [ClassicSimilarity], result of:
          0.030675275 = score(doc=2210,freq=2.0), product of:
            0.13082431 = queryWeight, product of:
              4.244485 = idf(docFreq=1723, maxDocs=44218)
              0.030822188 = queryNorm
            0.23447686 = fieldWeight in 2210, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              4.244485 = idf(docFreq=1723, maxDocs=44218)
              0.0390625 = fieldNorm(doc=2210)
      0.33333334 = coord(1/3)
  0.125 = coord(1/8)

Abstract

Sentiment analysis mainly focuses on the study of one's opinions that express positive or negative sentiments. With the explosive growth of web documents, sentiment analysis is becoming a hot topic in both academic research and system design. Fine-grained sentiment analysis is traditionally solved as a 2-step strategy, which results in cascade errors. Although joint models, such as joint sentiment/topic and maximum entropy (MaxEnt)/latent Dirichlet allocation, are proposed to tackle this problem of sentiment analysis, they focus on the joint learning of both aspects and sentiments. Thus, they are not appropriate to solve the cascade errors for sentiment analysis at the sentence or subsentence level. In this article, we present a novel jointly fine-grained sentiment analysis framework at the subsentence level with Markov logic. First, we divide the task into 2 separate stages (subjectivity classification and polarity classification). Then, the 2 separate stages are processed, respectively, with different feature sets, which are implemented by local formulas in Markov logic. Finally, global formulas in Markov logic are adopted to realize the interactions of the 2 separate stages. The joint inference of subjectivity and polarity helps prevent cascade errors. Experiments on a Chinese sentiment data set manifest that our joint model brings significant improvements.

0.0010534719 = product of:
  0.008427775 = sum of:
    0.008427775 = product of:
      0.025283325 = sum of:
        0.025283325 = weight(_text_:29 in 3464) [ClassicSimilarity], result of:
          0.025283325 = score(doc=3464,freq=2.0), product of:
            0.108422816 = queryWeight, product of:
              3.5176873 = idf(docFreq=3565, maxDocs=44218)
              0.030822188 = queryNorm
            0.23319192 = fieldWeight in 3464, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5176873 = idf(docFreq=3565, maxDocs=44218)
              0.046875 = fieldNorm(doc=3464)
      0.33333334 = coord(1/3)
  0.125 = coord(1/8)

Date

1. 6.2010 9:29:57

8.7789324E-4 = product of:
  0.007023146 = sum of:
    0.007023146 = product of:
      0.021069437 = sum of:
        0.021069437 = weight(_text_:29 in 3961) [ClassicSimilarity], result of:
          0.021069437 = score(doc=3961,freq=2.0), product of:
            0.108422816 = queryWeight, product of:
              3.5176873 = idf(docFreq=3565, maxDocs=44218)
              0.030822188 = queryNorm
            0.19432661 = fieldWeight in 3961, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5176873 = idf(docFreq=3565, maxDocs=44218)
              0.0390625 = fieldNorm(doc=3961)
      0.33333334 = coord(1/3)
  0.125 = coord(1/8)

Date

29. 8.2010 13:11:47

8.7789324E-4 = product of:
  0.007023146 = sum of:
    0.007023146 = product of:
      0.021069437 = sum of:
        0.021069437 = weight(_text_:29 in 3018) [ClassicSimilarity], result of:
          0.021069437 = score(doc=3018,freq=2.0), product of:
            0.108422816 = queryWeight, product of:
              3.5176873 = idf(docFreq=3565, maxDocs=44218)
              0.030822188 = queryNorm
            0.19432661 = fieldWeight in 3018, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5176873 = idf(docFreq=3565, maxDocs=44218)
              0.0390625 = fieldNorm(doc=3018)
      0.33333334 = coord(1/3)
  0.125 = coord(1/8)

Date

12. 6.2016 20:31:29