Tseng, Y.-H.: Automatic thesaurus generation for Chinese documents (2002)
0.02
0.01933244 = product of:
0.03866488 = sum of:
0.03866488 = product of:
0.07732976 = sum of:
0.07732976 = weight(_text_:news in 5226) [ClassicSimilarity], result of:
0.07732976 = score(doc=5226,freq=2.0), product of:
0.26705483 = queryWeight, product of:
5.2416887 = idf(docFreq=635, maxDocs=44218)
0.05094824 = queryNorm
0.28956512 = fieldWeight in 5226, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
5.2416887 = idf(docFreq=635, maxDocs=44218)
0.0390625 = fieldNorm(doc=5226)
0.5 = coord(1/2)
0.5 = coord(1/2)
- Abstract
- Tseng constructs a word co-occurrence based thesaurus by means of the automatic analysis of Chinese text. Words are identified by a longest dictionary match supplemented by a key word extraction algorithm that merges back nearby tokens and accepts shorter strings of characters if they occur more often than the longest string. Single character auxiliary words are a major source of error but this can be greatly reduced with the use of a 70-character 2680 word stop list. Extracted terms with their associate document weights are sorted by decreasing frequency and the top of this list is associated using a Dice coefficient modified to account for longer documents on the weights of term pairs. Co-occurrence is not in the document as a whole but in paragraph or sentence size sections in order to reduce computation time. A window of 29 characters or 11 words was found to be sufficient. A thesaurus was produced from 25,230 Chinese news articles and judges asked to review the top 50 terms associated with each of 30 single word query terms. They determined 69% to be relevant.