Egbert, J.; Biber, D.; Davies, M.: Developing a bottom-up, user-based method of web register classification (2015)
0.04
0.035819627 = product of:
0.14327851 = sum of:
0.14327851 = sum of:
0.10253391 = weight(_text_:project in 2158) [ClassicSimilarity], result of:
0.10253391 = score(doc=2158,freq=6.0), product of:
0.21156175 = queryWeight, product of:
4.220981 = idf(docFreq=1764, maxDocs=44218)
0.050121464 = queryNorm
0.48465237 = fieldWeight in 2158, product of:
2.4494898 = tf(freq=6.0), with freq of:
6.0 = termFreq=6.0
4.220981 = idf(docFreq=1764, maxDocs=44218)
0.046875 = fieldNorm(doc=2158)
0.0407446 = weight(_text_:22 in 2158) [ClassicSimilarity], result of:
0.0407446 = score(doc=2158,freq=2.0), product of:
0.17551683 = queryWeight, product of:
3.5018296 = idf(docFreq=3622, maxDocs=44218)
0.050121464 = queryNorm
0.23214069 = fieldWeight in 2158, 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=2158)
0.25 = coord(1/4)
- Abstract
- This paper introduces a project to develop a reliable, cost-effective method for classifying Internet texts into register categories, and apply that approach to the analysis of a large corpus of web documents. To date, the project has proceeded in 2 key phases. First, we developed a bottom-up method for web register classification, asking end users of the web to utilize a decision-tree survey to code relevant situational characteristics of web documents, resulting in a bottom-up identification of register and subregister categories. We present details regarding the development and testing of this method through a series of 10 pilot studies. Then, in the second phase of our project we applied this procedure to a corpus of 53,000 web documents. An analysis of the results demonstrates the effectiveness of these methods for web register classification and provides a preliminary description of the types and distribution of registers on the web.
- Date
- 4. 8.2015 19:22:04