Presentation is loading. Please wait.

Presentation is loading. Please wait.

Dr. Ahmad Abdollahzadeh

Similar presentations

Presentation on theme: "Dr. Ahmad Abdollahzadeh"— Presentation transcript:

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

Download ppt "Dr. Ahmad Abdollahzadeh"

Similar presentations

Ads by Google