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
Course Info
Instructors
Departments
As Taught In
Fall
2005
Level
Course Info
Instructors
Departments
As Taught In
Fall
2005
Level
Learning Resource Types
notes
Lecture Notes
assignment
Problem Sets