Search (3 results, page 1 of 1)

0.009544449 = product of:
  0.019088898 = sum of:
    0.019088898 = product of:
      0.07635559 = sum of:
        0.07635559 = weight(_text_:authors in 3462) [ClassicSimilarity], result of:
          0.07635559 = score(doc=3462,freq=4.0), product of:
            0.21438722 = queryWeight, product of:
              4.558814 = idf(docFreq=1258, maxDocs=44218)
              0.047026973 = queryNorm
            0.35615736 = fieldWeight in 3462, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              4.558814 = idf(docFreq=1258, maxDocs=44218)
              0.0390625 = fieldNorm(doc=3462)
      0.25 = coord(1/4)
  0.5 = coord(1/2)

Abstract

Discovering relationships among concepts and categories is crucial in various information systems. The authors' objective was to discover such relationships among document categories. Traditionally, such relationships are represented in the form of a concept hierarchy, grouping some categories under the same parent category. Although the nature of hierarchy supports the identification of categories that may share the same parent, not all of these categories have a relationship with each other - other than sharing the same parent. However, some non-sibling relationships exist that although are related to each other are not identified as such. The authors identify and build a relationship network (relationship-net) with categories as the vertices and relationships as the edges of this network. They demonstrate that using a relationship-net, some nonobvious category relationships are detected. Their approach capitalizes on the misclassification information generated during the process of text classification to identify potential relationships among categories and automatically generate relationship-nets. Their results demonstrate a statistically significant improvement over the current approach by up to 73% on 20 News groups 20NG, up to 68% on 17 categories in the Open Directories Project (ODP17), and more than twice on ODP46 and Special Interest Group on Information Retrieval (SIGIR) data sets. Their results also indicate that using misclassification information stemming from passage classification as opposed to document classification statistically significantly improves the results on 20NG (8%), ODP17 (5%), ODP46 (73%), and SIGIR (117%) with respect to F1 measure. By assigning weights to relationships and by performing feature selection, results are further optimized.

0.007964382 = product of:
  0.015928764 = sum of:
    0.015928764 = product of:
      0.031857528 = sum of:
        0.031857528 = weight(_text_:22 in 2765) [ClassicSimilarity], result of:
          0.031857528 = score(doc=2765,freq=2.0), product of:
            0.16468045 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.047026973 = queryNorm
            0.19345059 = fieldWeight in 2765, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.0390625 = fieldNorm(doc=2765)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Date

22. 3.2009 19:14:43

0.006748944 = product of:
  0.013497888 = sum of:
    0.013497888 = product of:
      0.053991552 = sum of:
        0.053991552 = weight(_text_:authors in 3957) [ClassicSimilarity], result of:
          0.053991552 = score(doc=3957,freq=2.0), product of:
            0.21438722 = queryWeight, product of:
              4.558814 = idf(docFreq=1258, maxDocs=44218)
              0.047026973 = queryNorm
            0.25184128 = fieldWeight in 3957, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              4.558814 = idf(docFreq=1258, maxDocs=44218)
              0.0390625 = fieldNorm(doc=3957)
      0.25 = coord(1/4)
  0.5 = coord(1/2)

Abstract

The large volume of biomedical literature poses a serious problem for medical professionals, who are often struggling to keep current with it. At the same time, many health providers consider knowledge of the latest literature in their field a key component for successful clinical practice. In this work, we introduce two systems designed to help retrieving medical literature. Both receive a long, discursive clinical note as input query, and return highly relevant literature that could be used in support of clinical practice. The first system is an improved version of a method previously proposed by the authors; it combines pseudo relevance feedback and a domain-specific term filter to reformulate the query. The second is an approach that uses a deep neural network to reformulate a clinical note. Both approaches were evaluated on the 2014 and 2015 TREC CDS datasets; in our tests, they outperform the previously proposed method by up to 28% in inferred NDCG; furthermore, they are competitive with the state of the art, achieving up to 8% improvement in inferred NDCG.