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Subspace, Latent Structure and Feature Selection techniques: Statistical and Optimisation perspectives Workshop

Semantic text features from small world graphs

author: Jure Leskovec, Carnegie Mellon University

Description

We present a set of methods for creating a semantic representation from a collection of textual documents. Given a document collection we use a simple algorithm to connect the documents into a tree or a graph. Using the imposed topology we define a feature and document similarity measures. We use the kernel alignment to compare the quality of various similarity measures. Results show that the document similarity defined over the topology gives better alignment than standard cosine similarity measure on a bag of words document representation.

Categories

Top: Computer Science: Semantic Web

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*Slides*
0:06	Semantic text features from small world graphs
0:15	Introduction
1:56	The general idea
2:29	Toy example
4:36	The algorithms
5:23	Algorithm 1: Basic Tree
6:10	Algorithm 1: Basic Tree (2)
6:32	Basic Tree: variations
7:08	Algorithm 2: Optimal Tree
7:38	Algorithm 3: Basic Graph
8:46	Feature similarity measure
9:52	Experimental setup
10:37	Experiments (1)
11:15	Experiments (2)
11:58	Experiments (3)
12:14	Experimental Results
13:05	Experimental Results
14:11	Conclusions and Future directions

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