I received the following email:
We are a training facility in Cape Town and are desperately looking for someone who can lecture the attached course. Could you kindly advise if you know of someone or perhaps yourself that could do lecturing for 3 weeks?
I have attached the course content.
Thank you for you time.
Kind Regards
Brumilda Engelbrecht
www.dtioffice.org
email:
brumilda@nkositravel.co.za
email:
dwayne@dtioffice.co.za
Tell: +27 (0) 87 807 8362
Cel: +27 (0) 82 559 0815
PC Fax: +27 (0) 86 270 2020
Some of the attached info:
Ambiguity
1. Introduction
2. Why is ambiguity a problem?
3. Local vs. global ambiguity
4. Types of ambiguity
4.1. Categorial ambiguity
4.2. Word sense ambiguity
4.3. Structural ambiguity
4.4. Referential ambiguity
4.5. Ellipsis
5. Serious problems to overcome
COURSE INTRODUCTION
This course presents an introduction to natural language processing, the primary concern of which is the study of human language use from a computational perspective. The course covers syntactic analysis, semantic interpretation, and discourse processing, examining both symbolic and statistical approaches. Possible topics include information extraction, natural language generation, memory models, ambiguity resolution, finite-state methods, mildly context-sensitive formalisms, deductive approaches to interpretation, machine translation, and machine learning of natural language.
COURSE CONTENTS
1 Introduction
- Applications of NLP techniques (MT, grammar checkers, dictation, document generation, NL interfaces)
- The different analysis levels used for NLP (morpho-lexical, syntactic, semantic, pragmatic)
- markup (TEI, UNICODE)
- Finite state automata
- Recursive and augmented transition networks
2 Lexical level
- Error-tolerant lexical processing (spelling error correction)
- Transducers for the design of morphologic analyzers
- Features
- Towards syntax: Part-of-speech tagging (Brill, HMM)
- Efficient representations for linguistic resources (lexica, grammars,...): tries and finite-state automata
3 Syntactic level
- Grammars (e.g. Formal/Chomsky hierarchy, DCGs, systemic, case, unification, stochastic)
- Parsing (top-down, bottom-up, chart (Earley algorithm), CYK algorithm)
- Automated estimation of probabilistic model parameters (inside-outside algorithm)
- Data Oriented Parsing
4 Semantic level
- Logical forms
- Ambiguity resolution
- Semantic networks and parsers
- Procedural semantics
- Montague semantics
- Vector Space approaches
- Distributional Semantics
5 Pragmatic level
- Knowledge representation
- Reasoning
- Plan/goal recognition
- speech acts/intentions
- belief models
- discourse
- reference
6 Natural language generation
- content determination
- sentence planning
- surface realisation
7 Other approaches
- statistical/corpus-based NLP
- connectionist NLP
If you need more info, contact her or me and I'll send you the full documents.
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"A man is the less likely to become great the more he is dominated by reason: few can achieve greatness - and none in art - if they are not dominated by illusion." Mr. Doctor
