6.864 | Fall 2005 | Graduate

Advanced Natural Language Processing

Lecture Notes



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.

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Lecture Notes

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