Bernier-Colborne, G.: Identifying semantic relations in a specialized corpus through distributional analysis of a cooccurrence tensor (2014)
0.00
0.004962362 = product of:
0.014887085 = sum of:
0.014887085 = weight(_text_:a in 2153) [ClassicSimilarity], result of:
0.014887085 = score(doc=2153,freq=12.0), product of:
0.05963374 = queryWeight, product of:
1.153047 = idf(docFreq=37942, maxDocs=44218)
0.051718395 = 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)
0.33333334 = coord(1/3)
- 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