Post on 19-Dec-2015
transcript
Amirkabir University of TechnologyComputer Engineering Faculty
Natural Language ProcessingCourse
Dr. Ahmad Abdollahzadeh
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Session Agenda Artificial Intelligence
Natural Language Processing
History of NLP
Applications of NLP
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AI Concepts and Definitions
• Encompasses Many Definitions • AI Involves Studying Human Thought
Processes• Representing Thought Processes on
Machines
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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. Aronson6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ
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AI Objectives
• Make machines smarter (primary goal)• Understand what intelligence is (Nobel
Laureate purpose)• Make machines more useful
(entrepreneurial purpose)
(Winston and Prendergast [1984])
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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
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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
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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
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Symbolic Processing
• Use Symbols to Represent Problem Concepts
• Apply Various Strategies and Rules to
Manipulate these Concepts
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AI Represents Knowledge as Sets of
SymbolsA symbol is a string of characters that stands for
some real-world concept
Examples• Product• Defendant• 0.8 • Chocolate
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Symbol Structures (Relationships)
• (DEFECTIVE product)• (LEASED-BY product
defendant)• (EQUAL (LIABILITY defendant)
0.8) • tastes_good (chocolate).
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• AI Programs Manipulate Symbols to Solve Problems
• Symbols and Symbol Structures Form Knowledge Representation
• Artificial Intelligence Dealings Primarily with Symbolic, Nonalgorithmic Problem- Solving Methods
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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
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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)
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Major AI Areas Expert Systems
Natural Language Processing Speech Understanding Robotics and Sensory Systems Computer Vision and Scene
Recognition Intelligent Computer-Aided
Instruction Neural Computing
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Additional AI Areas
News Summarization Language Translation Fuzzy Logic Genetic Algorithms Intelligent Software Agents
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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.
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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.
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Other Titles • The most common titles, apart from Natural
Language Processing include:
• Automatic Language Processing • Computational Linguistics • Natural Language Understanding
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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.
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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).
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NLP History (1) The first recognisable NLP application was a
dictionary look-up system developed at Birkbeck College, London in 1948.
NLP from 1966-1980 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.
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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.
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NLP History (3) NLP from 1980 - 1990
- Grammar Formalisms
NLP from 1990- now
- Multilinguality and Multimodality
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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
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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.
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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.
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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
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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.