Lecture Notes
lec07.pdf
Description:
This resource discusses about Semantic smilarity, motivation, computing semantic similarity, lexicons and semantic nets, WordNet, Synset example, WordNet relations, learning similarity from Corpora, Vector Space Model, similarity measure: euclidean and cosine, term weighting, cosine vs. euclidean, similarity for LM, Kullback Leibler Distance (relative entropy), problems with Corpus-based similarity, State-of-the-art methods, beyond pairwise similarity, hierarchical clustering, Agglomerative clustering, Single-Link clustering, Complete-Link clustering, K-Means algorithm, and comparing clustering by set matching.
Resource Type:
Lecture Notes
pdf
275 kB
lec07.pdf
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Fall
2005
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notes
Lecture Notes
assignment
Problem Sets