Machine Learning Summer School 2003 - Tuebingen

Information Retrieval and Language Technology

author: Thorsten Joachims, Cornell University

Description

The course will give an overview of how statistical learning can help organize and access information that is represented in textual form. In particular, it will cover tasks like text classification, information retrieval, information extraction, topic detection, and topic tracking. The course will introduce the basic techniques for representing text and analyze their statistical properties. An emphasis of the course will be on giving an overview of interesting learning problems in this area, providing starting points for future research.

Categories

Top: Computer Science: Information Retrieval
Top: Computer Science: Machine Learning: Human Language Technology

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*Slides*
0:01	Information Retrieval and Language Technology
3:56	Abstract
3:59	Overview
5:14	Part I: Information retreval basics
5:29	Overview: IR Basics
6:02	Information Retrieval
6:13	User Task
6:33	Basic IR Processes
7:47	Task Definition: Ad-hoc Retrieval
8:20	Overview: IR Basicsn
8:42	Text Representation
10:19	Example Document
10:28	Controlled Vocabularies
11:37	Controlled Vocabulary Indexing:Example
11:58	Controlled Vocabulary Indexing
12:48	Full-Text Indexing
13:34	Types of Retrieval Models:Exact Match vs. Best Match Retrieval
15:03	Popular Retrieval Models
18:30	Exact Match vs. Best Match Retrieval
20:02	Unranked Boolean Retrieval Model
20:18	Example
20:32	Ranked Vector Space Retrieval Model
21:36	Vector Space Representation
23:13	Vector Space Similarity
23:45	Vector Space Similarity
23:50	What Should be the Basis of the Vector Space?
26:17	Term Weights
27:35	Term Weights (TF)
28:45	Term Weights (IDF)
29:43	TFIDF Weights with Cosine
30:56	Settings for Ad-hoc Retrieval
32:51	Settings for Ad-hoc Retrieval
33:21	Overview: IR Basics
33:34	Evaluating Ad-hoc Retrieval Effectiveness
34:58	Relevance
36:00	Test Collections
36:09	Sample Test Collections
36:32	Finding Relevant Documents
36:36	Evaluation Metrics: Precision and Recall
39:00	Evaluation Metrics: Precision and Recall
39:15	Recall Precision Tables
40:29	Precision at Fixed Rank Cutoffs
41:11	F-measure
41:43	BreakEvenPoint
42:22	Overview: IR Basics

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