Machine Learning Summer School 2008 - Kioloa

Latent Variable Models for Document Analysis

author: Wray Buntine, Helsinki Institute of Information Technology

Description

Wray Buntine will consider various problems in document analysis (named entity recognition, natural language parsing, information retrieval), and look at various probabilistic graphical models and algorithms for addressing the problem. This will not be an extensive coverage of information extraction or natural language processing, but rather a look at some of the theory, methods and practice of particular cases, including the use of software environments.

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*Slides*
0:00	Latent Variable Models for Document Analysis
4:15	Part I - Motivation and Background
4:25	What a good Statistical NLP Course Needs
5:29	Outline (1)
6:26	Outline (2)
6:36	What is Formal Natural Language
7:30	Outline - Formal Natural Language - NLP Processing and Ambiguity
7:32	Analysing Language
10:33	Traditional NLP Processing
14:56	Common Tasks in NLP
15:28	NLP in Chinese
16:26	Translation Difficulties
17:38	Translation Difficulties, cont.
18:02	Language Ambiguities
19:06	Language Ambiguities, cont.
20:45	Language Ambiguities, cont.
24:18	Outline - Formal Natural Language - Words
24:20	Word Classes (dictionary version of part of speech)
24:45	Word Forms
24:54	Word Classes (dictionary version of part of speech)
25:14	Word Forms
29:30	Parts of Speech (computational version)
31:43	Parts of Speech (computational version), cont.
32:34	Collocations
34:57	Outline - Formal Natural Language - Parsing
35:20	Constituents
36:54	Parsing
40:02	Shallow Parsing
42:55	Outline
45:15	Language Ambiguities
45:19	Language Ambiguities, cont.

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