Search (2 results, page 1 of 1)

Did you mean:
precises%3a%2fdocuments %2f subject classification schemes%3a bliss%2c henry evelyn %2f bliss bibliographic classification %2f texts%22 2
precises%3a%2f_documents %2f subject classification schemes%3a bliss%2c henry evelyn %2f bliss bibliographic classification %2f texts%22 2
precises%3a%2fdocuments %2f subject classification schemes%3a bliss%2c heery evelyn %2f bliss bibliographic classification %2f texts%22 2
precises%3a%2fdocuments %2f subject classification schemes%3a bliss%2c henry evelyn %2f bloss bibliographic classification %2f texts%22 2
precises%3a%2fdocuments %2f subject classification schemes%3a bloss%2c henry evelyn %2f bliss bibliographic classification %2f texts%22 2

Yiqun, W.; Zhonghui, Z.; Li, Z.: Experimental study of machine factors and cognitive abilities of users (1998) 0.00

0.0024248946 = product of:
  0.033948522 = sum of:
    0.033948522 = weight(_text_:subject in 5225) [ClassicSimilarity], result of:
      0.033948522 = score(doc=5225,freq=2.0), product of:
        0.10738805 = queryWeight, product of:
          3.576596 = idf(docFreq=3361, maxDocs=44218)
          0.03002521 = queryNorm
        0.31612942 = fieldWeight in 5225, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.576596 = idf(docFreq=3361, maxDocs=44218)
          0.0625 = fieldNorm(doc=5225)
  0.071428575 = coord(1/14)

Abstract: Describes 3 experiments undertaken to research how machine factors influence the cognitive abilities of users. The experiments dealt with presentation programs of retrieval results and user selectivity, thesaurus construction and accuracy of the subject words ascertained by the user, and menu layout and time taken by the user. Discusses findings and the satisfaction of users with present databases

Tseng, Y.-H.: Solving vocabulary problems with interactive query expansion (1998) 0.00
```
0.0017956087 = product of:
  0.02513852 = sum of:
    0.02513852 = weight(_text_:bibliographic in 5159) [ClassicSimilarity], result of:
      0.02513852 = score(doc=5159,freq=2.0), product of:
        0.11688946 = queryWeight, product of:
          3.893044 = idf(docFreq=2449, maxDocs=44218)
          0.03002521 = queryNorm
        0.21506234 = fieldWeight in 5159, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.893044 = idf(docFreq=2449, maxDocs=44218)
          0.0390625 = fieldNorm(doc=5159)
  0.071428575 = coord(1/14)
```
Abstract

One of the major causes of search failures in information retrieval systems is vocabulary mismatch. Presents a solution to the vocabulary problem through 2 strategies known as term suggestion (TS) and term relevance feedback (TRF). In TS, collection specific terms are extracted from the text collection. These terms and their frequencies constitute the keyword database for suggesting terms in response to users' queries. One effect of this term suggestion is that it functions as a dynamic directory if the query is a general term that contains broad meaning. In term relevance feedback, terms extracted from the top ranked documents retrieved from the previous query are shown to users for relevance feedback. In the experiment, interactive TS provides very high precision rates while achieving similar recall rates as n-gram matching. Local TRF achieves improvement in both precision and recall rate in a full text news database and degrades slightly in recall rate in bibliographic databases due to the very limited source of information for feedback. In terms of Rijsbergen's combined measure of recall and precision, both TS and TRF achieve better performance than n-gram matching, which implies that the greater improvement in precision rate compensates the slight degradation in recall rate for TS and TRF

Search (2 results, page 1 of 1)

Authors

Themes