Search (149 results, page 8 of 8)

4.985905E-4 = product of:
  0.001994362 = sum of:
    0.001994362 = product of:
      0.005983086 = sum of:
        0.005983086 = weight(_text_:a in 4648) [ClassicSimilarity], result of:
          0.005983086 = score(doc=4648,freq=4.0), product of:
            0.055348642 = queryWeight, product of:
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.04800207 = queryNorm
            0.10809815 = fieldWeight in 4648, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.046875 = fieldNorm(doc=4648)
      0.33333334 = coord(1/3)
  0.25 = coord(1/4)

Abstract

The purpose of this paper is to determine semantic types for tags in social tagging systems. In social tagging systems, the determination of the semantic type of tags plays an important role in tag classification, increasing the semantic information of tags and establishing mapping relations between tagged resources and a normed ontology. The research reported in this paper constructs the semantic type library that is needed based on the Unified Medical Language System (UMLS) and FrameNet and determines the semantic type of selected tags that have been pretreated via direct matching using the Semantic Navigator tool, the Semantic Type Word Sense Disambiguation (STWSD) tools in UMLS, and artificial matching. And finally, we verify the feasibility of the determination of semantic type for tags by empirical analysis.

Type

a

4.700756E-4 = product of:
  0.0018803024 = sum of:
    0.0018803024 = product of:
      0.005640907 = sum of:
        0.005640907 = weight(_text_:a in 529) [ClassicSimilarity], result of:
          0.005640907 = score(doc=529,freq=2.0), product of:
            0.055348642 = queryWeight, product of:
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.04800207 = queryNorm
            0.10191591 = fieldWeight in 529, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.0625 = fieldNorm(doc=529)
      0.33333334 = coord(1/3)
  0.25 = coord(1/4)

4.700756E-4 = product of:
  0.0018803024 = sum of:
    0.0018803024 = product of:
      0.005640907 = sum of:
        0.005640907 = weight(_text_:a in 4798) [ClassicSimilarity], result of:
          0.005640907 = score(doc=4798,freq=2.0), product of:
            0.055348642 = queryWeight, product of:
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.04800207 = queryNorm
            0.10191591 = fieldWeight in 4798, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.0625 = fieldNorm(doc=4798)
      0.33333334 = coord(1/3)
  0.25 = coord(1/4)

Type

a

4.154921E-4 = product of:
  0.0016619684 = sum of:
    0.0016619684 = product of:
      0.0049859053 = sum of:
        0.0049859053 = weight(_text_:a in 3453) [ClassicSimilarity], result of:
          0.0049859053 = score(doc=3453,freq=4.0), product of:
            0.055348642 = queryWeight, product of:
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.04800207 = queryNorm
            0.090081796 = fieldWeight in 3453, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.0390625 = fieldNorm(doc=3453)
      0.33333334 = coord(1/3)
  0.25 = coord(1/4)

Abstract

In this article, we describe the results of an experiment designed to understand the effects of background information and social interaction on image tagging. The participants in the experiment were asked to tag 12 preselected images of Jewish cultural heritage. The users were partitioned into three groups: the first group saw only the images with no additional information whatsoever, the second group saw the images plus a short, descriptive title, and the third group saw the images, the titles, and the URL of the page in which the image appeared. In the first stage of the experiment, each user tagged the images without seeing the tags provided by the other users. In the second stage, the users saw the tags assigned by others and were encouraged to interact. Results show that after the social interaction phase, the tag sets converged and the popular tags became even more popular. Although in all cases the total number of assigned tags increased after the social interaction phase, the number of distinct tags decreased in most cases. When viewing the image only, in some cases the users were not able to correctly identify what they saw in some of the pictures, but they overcame the initial difficulties after interaction. We conclude from this experiment that social interaction may lead to convergence in tagging and that the wisdom of the crowds helps overcome the difficulties due to the lack of information.

Type

a

4.154921E-4 = product of:
  0.0016619684 = sum of:
    0.0016619684 = product of:
      0.0049859053 = sum of:
        0.0049859053 = weight(_text_:a in 5359) [ClassicSimilarity], result of:
          0.0049859053 = score(doc=5359,freq=4.0), product of:
            0.055348642 = queryWeight, product of:
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.04800207 = queryNorm
            0.090081796 = fieldWeight in 5359, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.0390625 = fieldNorm(doc=5359)
      0.33333334 = coord(1/3)
  0.25 = coord(1/4)

Abstract

Colored Tags (abbr.Tag) as a unique type of social tags is used on e-commerce websites (e.g., Taobao) to summarize the high-frequency keywords extracted from users' online reviews about products they bought before. Tag is represented inked red or green according to users' personal experiences and judgments about purchased items: red for positive comments, green for negative ones. The valence of users' emotion induced by red or green is controversial. This study firstly discovers that colored tags inked in red incite users' positive emotion (evaluations) and colored tags inked in green incite negative emotion (evaluations) using an ERP experiment, which is manifested in ERP components (e.g., N170, N2c, and LPC). There are two main features of Tag: the text of Tag (abbr. Text) and the color of Tag (abbr.Color). Our study then proves that Color (red or green) is the dominant factor in users' decision behavior compared with Text under the high cognitive load condition, while users' decision behavior is influenced by Text (positive tags or negative tags) predominately rather than by Color under the low cognitive load condition with the help of Eye tracking instrument. Those findings can help to design colored tags for recommendation systems on e-commerce websites and other online platforms.

Type

a

4.1131617E-4 = product of:
  0.0016452647 = sum of:
    0.0016452647 = product of:
      0.004935794 = sum of:
        0.004935794 = weight(_text_:a in 5233) [ClassicSimilarity], result of:
          0.004935794 = score(doc=5233,freq=2.0), product of:
            0.055348642 = queryWeight, product of:
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.04800207 = queryNorm
            0.089176424 = fieldWeight in 5233, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.0546875 = fieldNorm(doc=5233)
      0.33333334 = coord(1/3)
  0.25 = coord(1/4)

Type

a

3.5255676E-4 = product of:
  0.001410227 = sum of:
    0.001410227 = product of:
      0.004230681 = sum of:
        0.004230681 = weight(_text_:a in 2364) [ClassicSimilarity], result of:
          0.004230681 = score(doc=2364,freq=2.0), product of:
            0.055348642 = queryWeight, product of:
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.04800207 = queryNorm
            0.07643694 = fieldWeight in 2364, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.046875 = fieldNorm(doc=2364)
      0.33333334 = coord(1/3)
  0.25 = coord(1/4)

Content

Inhalt: What is social software? -- Blogs -- Blogs in libraries : practical applications -- RSS -- Wikis -- Online communities -- Social networking -- Social bookmarking and collaborative filtering -- Tools for synchronous online reference -- The mobile revolution -- Podcasting -- Screencasting and vodcasting -- Gaming -- What will work @ your library -- Keeping up : a primer -- Future trends in social software.

3.5255676E-4 = product of:
  0.001410227 = sum of:
    0.001410227 = product of:
      0.004230681 = sum of:
        0.004230681 = weight(_text_:a in 3421) [ClassicSimilarity], result of:
          0.004230681 = score(doc=3421,freq=2.0), product of:
            0.055348642 = queryWeight, product of:
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.04800207 = queryNorm
            0.07643694 = fieldWeight in 3421, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.046875 = fieldNorm(doc=3421)
      0.33333334 = coord(1/3)
  0.25 = coord(1/4)

Type

a

2.9379726E-4 = product of:
  0.001175189 = sum of:
    0.001175189 = product of:
      0.003525567 = sum of:
        0.003525567 = weight(_text_:a in 2813) [ClassicSimilarity], result of:
          0.003525567 = score(doc=2813,freq=2.0), product of:
            0.055348642 = queryWeight, product of:
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.04800207 = queryNorm
            0.06369744 = fieldWeight in 2813, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.0390625 = fieldNorm(doc=2813)
      0.33333334 = coord(1/3)
  0.25 = coord(1/4)

Abstract

Today, in times of Web 2.0., graduates and undergraduates interact in virtual communities like studiVZ (Studentenverzeichnis) and generate content by reviewing or tagging documents. This phenomenon offers good prospects for academic libraries. They can use the customers' tags for indexing the growing amount of electronic resources and thereby optimize the search for these documents. Important examples are the journals, databases and e-books included in the "Nationallizenzen" financed by the German Research Foundation (DFG). The documents in this collection are not manually indexed by librarians and have no annotation according to the German standard classification systems. Connecting search systems by means of Web-2.0.-services is an important task for libraries. For this purpose users are encouraged to tag printed and electronic resources in search systems like the libraries' online catalogs and to establish connections between entries in other systems, e.g. Bibsonomy, and the items found in the online catalog. As a consequence annotations chosen by both, users and librarians, will coexist: The items in the tagging systems and the online catalog are linked, library users may find other publications of interest, and contacts between library users with similar scientific interests may be established. Librarians have to face the fact that user generated tags do not necessarily have the same quality as their own annotations and will therefore have to seek for instruments for comparing user generated tags with library generated keywords.