1. Dr. Ahmad Abdollahzadeh
Amirkabir University of Technology Computer Engineering Faculty Natural Language Processing Course
Dr. Ahmad Abdollahzadeh

2. Session Agenda Artificial Intelligence Natural Language Processing
History of NLP
Applications of NLP

3. AI Concepts and Definitions
Encompasses Many Definitions
AI Involves Studying Human Thought Processes
Representing Thought Processes on Machines

4. Artificial Intelligence
Behavior by a machine that, if performed by a human being, would be considered intelligent
“…study of how to make computers do things at which, at the moment, people are better” (Rich and Knight [1991])
Theory of how the human mind works (Mark Fox)
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

5. (Winston and Prendergast [1984])
AI Objectives
Make machines smarter (primary goal)
Understand what intelligence is (Nobel Laureate purpose)
Make machines more useful (entrepreneurial purpose)
(Winston and Prendergast [1984])

6. Signs of Intelligence Learn or understand from experience
Make sense out of ambiguous or contradictory messages
Respond quickly and successfully to new situations
Use reasoning to solve problems

7. More Signs of Intelligence
Deal with perplexing situations
Understand and infer in ordinary, rational ways
Apply knowledge to manipulate the environment
Think and reason
Recognize the relative importance of different elements in a situation

8. Turing Test for Intelligence
A computer can be considered to be smart only when a human interviewer, “conversing” with both an unseen human being and an unseen computer, can not determine which is which

9. Symbolic Processing Use Symbols to Represent Problem Concepts
Apply Various Strategies and Rules to Manipulate these Concepts

10. AI Represents Knowledge as Sets of Symbols
A symbol is a string of characters that stands for some real-world concept
Examples
Product
Defendant
0.8
Chocolate

11. Symbol Structures (Relationships)
(DEFECTIVE product)
(LEASED-BY product defendant)
(EQUAL (LIABILITY defendant) 0.8)
tastes_good (chocolate).

12. AI Programs Manipulate Symbols to Solve Problems
Symbols and Symbol Structures Form Knowledge Representation
Artificial Intelligence Dealings Primarily with Symbolic, Nonalgorithmic Problem- Solving Methods

13. AI Computing Based on symbolic representation and manipulation
A symbol is a letter, word, or number representing objects, processes, and their relationships
Objects can be people, things, ideas, concepts, events, or statements of fact
Creates a symbolic knowledge base

14. AI Computing (cont’d) Manipulates symbols to generate advice
AI reasons or infers with the knowledge base by search and pattern matching
Hunts for answers (via algorithms)

15. Major AI Areas Natural Language Processing Expert Systems
Speech Understanding
Robotics and Sensory Systems
Computer Vision and Scene Recognition
Intelligent Computer-Aided Instruction
Neural Computing

16. Additional AI Areas News Summarization Language Translation
Fuzzy Logic
Genetic Algorithms
Intelligent Software Agents

17. Natural Language?
Natural language is the language we write and speak in everyday social interaction.
There are of course many varieties of natural language
It is quite possible to argue that the spoken and the written forms of the language are different and may be largely independent.
There are systems of vocabulary, syntax and semantics which can be observed (or similarly discovered) and recorded.
Those working in NLP also would claim (or at least hope) that it is possible to "automate" these descriptions to produce useful systems that are based on these descriptions.

18. Natural Language Processing (NLP)
Natural language processing concerns the development of computational models of aspects of human language processing such as :
Reading and interpreting a textbook
Writing a letter
Holding a conversation
Translating a document
Searching for useful information
Such models are useful in order to write computer programs to perform useful tasks involving language processing and in order to develop a better understanding of human communication.

19. Other Titles
The most common titles, apart from Natural Language Processing include:
Automatic Language Processing
Computational Linguistics
Natural Language Understanding

20. Computational Lingusitics
This is the application of computers to the scientific study of human language.
This definition suggests that there are connections with Cognitive Science, that is to say, the study of how humans produce and understand language.
Historically, Computational Linguistics has been associated with work in Generative Linguistics and formerly included the study of formal languages (eg finite state automata) and programming languages.
The computer is used as a tool on which models can be developed and evaluated, for instance implementations of theories of child language acquisition.

21. Natural Language Understanding
Distinguish a particular approach to Natural Language Processing.
The people using this title tend to lay much emphasis on the meaning of the language being processed, in particular getting the computer to respond to the input in an apparently intelligent fashion.
At one time, those who belonged to the Natural Language Understanding camp avoided the use of any syntactic processing, but textbooks that bear this title now include significant sections on syntactic processing, which suggests that the edge of the title has been rather blunted. (For instance, see Allen (1987; part 1).

22. NLP History (1)
The first recognisable NLP application was a dictionary look-up system developed at Birkbeck College, London in 1948.
NLP from
Augmented Transition Networks
The Augmented Transition Network (ATN) is a piece of searching software that is capable of using very powerful grammars to process syntax.
Case Grammar
The significance of the proposal for NLP is that it contributed a relatively easily implementable theory which could contribute
much semantic information with little processing effort. It also
contributed to the solution of one of theintractable problems of Machine Translation: thetranslation of prepositions.

23. NLP History (2) NLP from 1966-1980 Semantic representations
Schank and his workers introduced the notion of Conceptual Dependency, a method of expressing language in terms of semantic primitives. Systems were written which included no syntactic processing.
QuillianÕs work on memory introduced the idea of the semantic network, which has been used in varying forms for knowledge representation in many systems.
William Woods used the idea of procedural semantics to act as an intermediate representation between a language processing system and a database system.
The key systems were:
SHRDLU
LUNAR: A database interface system that used ATNs and Woods' Procedural Semantics.
LIFER/LADDER: One of the most impressive of NLP systems. It was designed as a natural language interface to a database of information about US Navy ships.

24. NLP History (3) NLP from 1980 - 1990 - Grammar Formalisms
NLP from now
- Multilinguality and Multimodality

25. NLP Applications
Applications can be classified in different ways, e.g. medium/modality; depth of analysis;degree of interaction
Text-based applications
NL Understanding
Dialogue Systems
Multimodal

26. Text-based Applications
Processing of written texts such as books,news, papers,reports:
Finding appropriate documents on certain topics from a text database
Extracting information from messages,articles, Web pages, etc.
Translating documents from one language to another
Text summarisation
Note: Not all such applications require NLP
Keyword based techniques can suce for identifying particular subject areas, e.g. legal, financial, etc.

27. NL Understanding
Other kinds of request require a deeper level of analysis
Find me all articles concerning car accidents involving more than two cars in Malta during the first half of 2001
Here the system must extract enough information to determine whether the article meets the criterion defined by the query.
A crucial characteristic of an understanding system is that it can compute some representation of the information that can be used for later inference
A crucial question for an NLP system is how much understanding is necessary to achieve the purpose of the system.

28. Dialogue-based Applications
Dialogue-based applications involve man-machine communication
NL database query systems
Automated customer services, e.g. banking services
General NL mediated problem solving systems
Some of the differences between dialogue and text-based systems:
Language used is less formal
System needs to act proactively in order to maintain smooth conversation
Use of acknowledgements clarication sub-dialogues

29. Text-based Applications
Processing of written texts such as books,news, papers,reports:
Finding appropriate documents on certain topics from a text database
Extracting information from messages,articles, Web pages, etc.
Translating documents from one language to another
Text summarisation
Note: Not all such applications require NLP
Keyword based techniques can suce for identifying particular subject areas, e.g. legal, financial, etc.

30. Multimodal Applications
Involve two or more modalities of communication
Text
Speech
Gesture
Image
Text  speech
Speech  text
Multimodal document generation
Spoken translation systems
Spoken dialogue systems