Bernier-Colborne, G.: Identifying semantic relations in a specialized corpus through distributional analysis of a cooccurrence tensor (2014)
0.01
0.007864855 = product of:
0.019662138 = sum of:
0.013347079 = weight(_text_:a in 2153) [ClassicSimilarity], result of:
0.013347079 = score(doc=2153,freq=12.0), product of:
0.053464882 = queryWeight, product of:
1.153047 = idf(docFreq=37942, maxDocs=44218)
0.046368346 = queryNorm
0.24964198 = fieldWeight in 2153, product of:
3.4641016 = tf(freq=12.0), with freq of:
12.0 = termFreq=12.0
1.153047 = idf(docFreq=37942, maxDocs=44218)
0.0625 = fieldNorm(doc=2153)
0.006315058 = product of:
0.012630116 = sum of:
0.012630116 = weight(_text_:information in 2153) [ClassicSimilarity], result of:
0.012630116 = score(doc=2153,freq=2.0), product of:
0.08139861 = queryWeight, product of:
1.7554779 = idf(docFreq=20772, maxDocs=44218)
0.046368346 = queryNorm
0.1551638 = fieldWeight in 2153, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
1.7554779 = idf(docFreq=20772, maxDocs=44218)
0.0625 = fieldNorm(doc=2153)
0.5 = coord(1/2)
0.4 = coord(2/5)
- Abstract
- We describe a method of encoding cooccurrence information in a three-way tensor from which HAL-style word space models can be derived. We use these models to identify semantic relations in a specialized corpus. Results suggest that the tensor-based methods we propose are more robust than the basic HAL model in some respects.
- Type
- a