Search (11 results, page 1 of 1)

0.015178238 = product of:
  0.030356476 = sum of:
    0.030356476 = product of:
      0.060712952 = sum of:
        0.060712952 = weight(_text_:classification in 356) [ClassicSimilarity], result of:
          0.060712952 = score(doc=356,freq=6.0), product of:
            0.16603322 = queryWeight, product of:
              3.1847067 = idf(docFreq=4974, maxDocs=44218)
              0.05213454 = queryNorm
            0.3656675 = fieldWeight in 356, product of:
              2.4494898 = tf(freq=6.0), with freq of:
                6.0 = termFreq=6.0
              3.1847067 = idf(docFreq=4974, maxDocs=44218)
              0.046875 = fieldNorm(doc=356)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

Conversational interfaces are increasingly popular as a way of connecting people to information. With the increased generative capacity of corpus-based conversational agents comes the need to classify and filter out malevolent responses that are inappropriate in terms of content and dialogue acts. Previous studies on the topic of detecting and classifying inappropriate content are mostly focused on a specific category of malevolence or on single sentences instead of an entire dialogue. We make three contributions to advance research on the malevolent dialogue response detection and classification (MDRDC) task. First, we define the task and present a hierarchical malevolent dialogue taxonomy. Second, we create a labeled multiturn dialogue data set and formulate the MDRDC task as a hierarchical classification task. Last, we apply state-of-the-art text classification methods to the MDRDC task, and report on experiments aimed at assessing the performance of these approaches.

0.014605265 = product of:
  0.02921053 = sum of:
    0.02921053 = product of:
      0.05842106 = sum of:
        0.05842106 = weight(_text_:classification in 345) [ClassicSimilarity], result of:
          0.05842106 = score(doc=345,freq=2.0), product of:
            0.16603322 = queryWeight, product of:
              3.1847067 = idf(docFreq=4974, maxDocs=44218)
              0.05213454 = queryNorm
            0.35186368 = fieldWeight in 345, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.1847067 = idf(docFreq=4974, maxDocs=44218)
              0.078125 = fieldNorm(doc=345)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Footnote

Rez. in: Cataloging & Classification Quarterly, Volume 49(2011) no.1, S.47-49 (William Denton).

0.012486639 = product of:
  0.024973279 = sum of:
    0.024973279 = product of:
      0.049946558 = sum of:
        0.049946558 = weight(_text_:22 in 2808) [ClassicSimilarity], result of:
          0.049946558 = score(doc=2808,freq=4.0), product of:
            0.18256627 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.05213454 = queryNorm
            0.27358043 = fieldWeight in 2808, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.0390625 = fieldNorm(doc=2808)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Date

30. 1.1999 17:22:22

0.010595265 = product of:
  0.02119053 = sum of:
    0.02119053 = product of:
      0.04238106 = sum of:
        0.04238106 = weight(_text_:22 in 2360) [ClassicSimilarity], result of:
          0.04238106 = score(doc=2360,freq=2.0), product of:
            0.18256627 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.05213454 = queryNorm
            0.23214069 = fieldWeight in 2360, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.046875 = fieldNorm(doc=2360)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

This article reports the author's recent research in developing a holistic model for various levels of digital library (DL) evaluation in which perceived important criteria from heterogeneous stakeholder groups are organized and presented. To develop such a model, the author applied a three-stage research approach: exploration, confirmation, and verification. During the exploration stage, a literature review was conducted followed by an interview, along with a card sorting technique, to collect important criteria perceived by DL experts. Then the criteria identified were used for developing an online survey during the confirmation stage. Survey respondents (431 in total) from 22 countries rated the importance of the criteria. A holistic DL evaluation model was constructed using statistical techniques. Eventually, the verification stage was devised to test the reliability of the model in the context of searching and evaluating an operational DL. The proposed model fills two lacunae in the DL domain: (a) the lack of a comprehensive and flexible framework to guide and benchmark evaluations, and (b) the uncertainty about what divergence exists among heterogeneous DL stakeholders, including general users.

0.010595265 = product of:
  0.02119053 = sum of:
    0.02119053 = product of:
      0.04238106 = sum of:
        0.04238106 = weight(_text_:22 in 2742) [ClassicSimilarity], result of:
          0.04238106 = score(doc=2742,freq=2.0), product of:
            0.18256627 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.05213454 = queryNorm
            0.23214069 = fieldWeight in 2742, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.046875 = fieldNorm(doc=2742)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Date

22. 3.2009 17:49:11

0.010223686 = product of:
  0.020447372 = sum of:
    0.020447372 = product of:
      0.040894743 = sum of:
        0.040894743 = weight(_text_:classification in 1924) [ClassicSimilarity], result of:
          0.040894743 = score(doc=1924,freq=2.0), product of:
            0.16603322 = queryWeight, product of:
              3.1847067 = idf(docFreq=4974, maxDocs=44218)
              0.05213454 = queryNorm
            0.24630459 = fieldWeight in 1924, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.1847067 = idf(docFreq=4974, maxDocs=44218)
              0.0546875 = fieldNorm(doc=1924)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Source

Cataloging and classification quarterly. 50(2012) no.5/7, S.705-723

0.008829388 = product of:
  0.017658776 = sum of:
    0.017658776 = product of:
      0.03531755 = sum of:
        0.03531755 = weight(_text_:22 in 993) [ClassicSimilarity], result of:
          0.03531755 = score(doc=993,freq=2.0), product of:
            0.18256627 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.05213454 = queryNorm
            0.19345059 = fieldWeight in 993, 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=993)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Date

22. 6.2023 18:18:34

0.008829388 = product of:
  0.017658776 = sum of:
    0.017658776 = product of:
      0.03531755 = sum of:
        0.03531755 = weight(_text_:22 in 994) [ClassicSimilarity], result of:
          0.03531755 = score(doc=994,freq=2.0), product of:
            0.18256627 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.05213454 = queryNorm
            0.19345059 = fieldWeight in 994, 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=994)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Date

22. 6.2023 18:07:12

0.00876316 = product of:
  0.01752632 = sum of:
    0.01752632 = product of:
      0.03505264 = sum of:
        0.03505264 = weight(_text_:classification in 2082) [ClassicSimilarity], result of:
          0.03505264 = score(doc=2082,freq=2.0), product of:
            0.16603322 = queryWeight, product of:
              3.1847067 = idf(docFreq=4974, maxDocs=44218)
              0.05213454 = queryNorm
            0.21111822 = fieldWeight in 2082, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.1847067 = idf(docFreq=4974, maxDocs=44218)
              0.046875 = fieldNorm(doc=2082)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

Language modeling is an effective and theoretically attractive probabilistic framework for text information retrieval. The basic idea of this approach is to estimate a language model of a given document (or document set), and then do retrieval or classification based on this model. A common language modeling approach assumes the data D is generated from a mixture of several language models. The core problem is to find the maximum likelihood estimation of one language model mixture, given the fixed mixture weights and the other language model mixture. The EM algorithm is usually used to find the solution. In this paper, we proof that an exact maximum likelihood estimation of the unknown mixture component exists and can be calculated using the new algorithm we proposed. We further improve the algorithm and provide an efficient algorithm of O(k) complexity to find the exact solution, where k is the number of words occurring at least once in data D. Furthermore, we proof the probabilities of many words are exactly zeros, and the MLE estimation is implemented as a feature selection technique explicitly.

0.00876316 = product of:
  0.01752632 = sum of:
    0.01752632 = product of:
      0.03505264 = sum of:
        0.03505264 = weight(_text_:classification in 1262) [ClassicSimilarity], result of:
          0.03505264 = score(doc=1262,freq=2.0), product of:
            0.16603322 = queryWeight, product of:
              3.1847067 = idf(docFreq=4974, maxDocs=44218)
              0.05213454 = queryNorm
            0.21111822 = fieldWeight in 1262, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.1847067 = idf(docFreq=4974, maxDocs=44218)
              0.046875 = fieldNorm(doc=1262)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

Understanding specific patterns or knowledge of self-disclosing health information could support public health surveillance and healthcare. This study aimed to develop an analytical framework to identify self-disclosing health information with unusual messages on web forums by leveraging advanced text-mining techniques. To demonstrate the performance of the proposed analytical framework, we conducted an experimental study on 2 major human immunodeficiency virus (HIV)/acquired immune deficiency syndrome (AIDS) forums in Taiwan. The experimental results show that the classification accuracy increased significantly (up to 83.83%) when using features selected by the information gain technique. The results also show the importance of adopting domain-specific features in analyzing unusual messages on web forums. This study has practical implications for the prevention and support of HIV/AIDS healthcare. For example, public health agencies can re-allocate resources and deliver services to people who need help via social media sites. In addition, individuals can also join a social media site to get better suggestions and support from each other.

0.0073026326 = product of:
  0.014605265 = sum of:
    0.014605265 = product of:
      0.02921053 = sum of:
        0.02921053 = weight(_text_:classification in 1180) [ClassicSimilarity], result of:
          0.02921053 = score(doc=1180,freq=2.0), product of:
            0.16603322 = queryWeight, product of:
              3.1847067 = idf(docFreq=4974, maxDocs=44218)
              0.05213454 = queryNorm
            0.17593184 = fieldWeight in 1180, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.1847067 = idf(docFreq=4974, maxDocs=44218)
              0.0390625 = fieldNorm(doc=1180)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

Searches for specific factual health information constitute a significant part of consumer health information requests, but little is known about how users search for such information. This study attempts to fill this gap by observing users' behavior while using MedlinePlus to search for specific health information. Nineteen students participated in the study, and each performed 12 specific tasks. During the search process, they submitted short queries or complete questions, and they examined less than 1 result per search. Participants rarely reformulated queries; when they did, they tended to make a query more specific or more general, or iterate in different ways. Participants also browsed, primarily relying on the alphabetical list and the anatomical classification, to navigate to specific health topics. Participants overall had a positive experience with MedlinePlus, and the experience was significantly correlated with task difficulty and participants' spatial abilities. The results suggest that, to better support specific item search in the health domain, systems could provide a more "natural" interface to encourage users to ask questions; effective conceptual hierarchies could be implemented to help users reformulate queries; and the search results page should be reconceptualized as a place for accessing answers rather than documents. Moreover, multiple schemas should be provided to help users navigate to a health topic. The results also suggest that users' experience with information systems in general and health-related systems in particular should be evaluated in relation to contextual factors, such as task features and individual differences.