The 13th International Conference on Knowledge Discovery and Data Mining

Text Mining and Link Analysis for Web and Semantic Web

author: Marko Grobelnik, Jozef Stefan Institute
coauthor: Blaž Fortuna, Jozef Stefan Institute

Description

The tutorial on Text Mining and Link Analysis for Web Data will focus on two main analytical approaches when analyzing web data: text mining and link analysis for the purpose of analyzing web documents and their linkage. First, the tutorial will cover some basic steps and problems when dealing with the textual and network (graph) data showing what is possible to achieve without very sophisticated technology. The idea of this first part is to present the nature of un-structured and semi-structured data. Next, in the second part, more sophisticated methods for solving more difficult and challenging problems will be shown. In the last part, some of the current open research issues will be presented and some practical pointers on the available tolls for solving previously mentioned problems will be provided.

Categories

Top: Computer Science: Text Mining
Top: Computer Science: Semantic Web

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*Slides*
0:03	Tutorial on Text Mining and Link Analysis for Web and Semantic Web
0:45	Outline
2:09	Text-Mining
2:11	Why Do We Analyze Text?
3:09	What Is Text-Mining?
3:47	Why Dealing with Text Is Tough?
4:58	Why Dealing with Text Is Easy?
5:52	Who Is in the Text Analysis Arena?
7:40	What Dimensions Are in Text Analytics?
9:08	How Dimensions Fit to Research Areas?
9:55	Broader Context: Web Science
10:50	Text-Mining - How Do We Represent Text?
10:57	Levels of Text Representations pt 1
12:39	Levels of Text Representations pt 2
12:43	Character Level
13:21	Good and Bad Sides
14:58	Levels of Text Representations pt 3
15:04	Word Level
15:46	Words Properties
17:47	Stop-Words
18:25	Word Character Level Normalization
19:08	Stemming (1/2)
19:44	Stemming (2/2)
21:03	Levels of Text Representations pt 4
21:13	Phrase Level
22:17	Google N-Gram Corpus
23:03	Example: Google N-Grams
23:20	Levels of Text Representations pt 5
23:39	Part-of-Speech Level
24:44	Part-of-Speech Table
25:20	Part-of-Speech Examples
25:52	Levels of Text Representations pt 6
26:01	Taxonomies/Thesaurus Level
26:49	WordNet – Database of Lexical Relations
27:28	WordNet – Excerpt from the Graph
28:03	WordNet Relations
28:07	WordNet – Excerpt from the Graph (a)
28:16	WordNet Relations (a)
29:05	Levels of Text Representations pt 7
29:15	Vector-Space Model Level
30:28	Bag-of-Words Document Representation
31:00	Word Weighting
32:19	Example Document and Its Vector Representation
32:43	Similarity between Document Vectors
33:53	Levels of Text Representations pt 8
34:01	Language Model Level
35:07	Levels of Text Representations pt 9
35:11	Full-Parsing Level
36:12	Levels of Text Representations pt 10
36:25	Cross-Modality Level
37:23	Example: Aligning Text with Audio, Images and Video
38:43	Levels of Text Representations pt 11
38:58	Collaborative Tagging
39:56	Example: flickr.com Tagging
40:23	Example: del.icio.us Tagging
40:27	Example: flickr.com Tagging (a)
40:29	Example: del.icio.us Tagging (a)
41:18	Levels of Text Representations pt 12
41:23	Template / Frames Level
41:36	Examples of Templates of KnowItAll System
42:55	Levels of Text Representations pt 13
43:02	Ontologies Level
43:23	Example: Text Represented in the First Order Logic
44:28	Text-Mining - Typical Tasks on Text
44:51	Document Summarization pt 1
44:52	Document Summarization pt 2
45:55	Selection Based Summarization
47:33	Example of Selection Based Approach from MS Word
48:04	Knowledge Rich Summarization
48:32	Knowledge Rich Summarization Example
50:10	Training of Summarization Model
50:39	Example of Summarization
50:45	Automatically Generated Graph of Summary Triples
53:18	Text Segmentation pt 1
53:20	Text Segmentation pt 2
53:21	Hearst Algorithm for Text Segmentation
53:23	Supervised Learning
53:48	Document Categorization Task
54:45	Document Categorization
55:24	Algorithms for Learning Document Classifiers
56:01	Example Learning Algorithm: Perceptron

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