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

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