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Natural Language Processing Heshaam Feili July 2003.

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Presentation on theme: "Natural Language Processing Heshaam Feili July 2003."— Presentation transcript:

1 Natural Language Processing Heshaam Feili July 2003

2 Natural language Processing (Heshaam Feili – July 2003) 2 Session Agenda  Artificial Intelligence  Natural Language Processing  History of NLP  Applications of NLP

3 Natural language Processing (Heshaam Feili – July 2003) 3 AI Concepts and Definitions Encompasses Many Definitions AI Involves Studying Human Thought Processes Representing Thought Processes on Machines

4 Natural language Processing (Heshaam Feili – July 2003) 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)

5 Natural language Processing (Heshaam Feili – July 2003) 5 AI Objectives Make machines smarter (primary goal) Understand what intelligence is (Nobel Laureate purpose) Make machines more useful (practical purpose) (Winston and Prendergast [1984])

6 Natural language Processing (Heshaam Feili – July 2003) 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 Natural language Processing (Heshaam Feili – July 2003) 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 Natural language Processing (Heshaam Feili – July 2003) 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 Natural language Processing (Heshaam Feili – July 2003) 9 Symbolic Processing Use Symbols to Represent Problem Concepts Apply Various Strategies and Rules to Manipulate these Concepts

10 Natural language Processing (Heshaam Feili – July 2003) 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 Natural language Processing (Heshaam Feili – July 2003) 11 Symbol Structures (Relationships) (DEFECTIVE product) (EQUAL (LIABILITY defendant) 0.8) tastes_good (chocolate).

12 Natural language Processing (Heshaam Feili – July 2003) 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 Natural language Processing (Heshaam Feili – July 2003) 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 Natural language Processing (Heshaam Feili – July 2003) 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 Natural language Processing (Heshaam Feili – July 2003) 15 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

16 Natural language Processing (Heshaam Feili – July 2003) 16 Additional AI Areas  News Summarization  Language Translation  Fuzzy Logic  Genetic Algorithms  Intelligent Software Agents

17 Natural language Processing (Heshaam Feili – July 2003) NLP ? Natural Language is one of fundamental aspects of human behaviors. One of the final aim of human- computer communication. Provide easy interaction with computer Make computer to understand texts.

18 Natural language Processing (Heshaam Feili – July 2003) Major Disciplines Studying Language DisciplineTypical Problem Linguists How do words from phrases and sentences? Psycholinguists How do people identify the structure of sentences? Philosophers What is meaning and how do words and sentences acquires it? Computational Linguists How is the structure of sentences identified?

19 Natural language Processing (Heshaam Feili – July 2003) Interaction Level The level that computer and human interact. NL used for make Interaction level near to human. HumanComputer Command-line NL UI Graphical UI Interaction level

20 Natural language Processing (Heshaam Feili – July 2003) 20 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.

21 Natural language Processing (Heshaam Feili – July 2003) 21 Other Titles The most common titles, apart from Natural Language Processing include: Automatic Language Processing Computational Linguistics Natural Language Understanding

22 Natural language Processing (Heshaam Feili – July 2003) 22 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.

23 Natural language Processing (Heshaam Feili – July 2003) Computational Lingusitics  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.

24 Natural language Processing (Heshaam Feili – July 2003) 24 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.

25 Natural language Processing (Heshaam Feili – July 2003) Natural Language Understanding  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).Allen (1987

26 Natural language Processing (Heshaam Feili – July 2003) 26 NLP History (1)  The first recognizable NLP application was a dictionary look- up system developed at Birkbeck College, London in  NLP from  Augmented Transition Networks  Case Grammar

27 Natural language Processing (Heshaam Feili – July 2003) 27 NLP History (2)  NLP from  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.

28 Natural language Processing (Heshaam Feili – July 2003) 28 NLP History (3)  The key systems were:  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.  NLP from Grammar Formalisms  NLP from now - Multilinguality and Multimodality

29 Natural language Processing (Heshaam Feili – July 2003) 29 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

30 Natural language Processing (Heshaam Feili – July 2003) 30 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.

31 Natural language Processing (Heshaam Feili – July 2003) Text-based Applications  Translating documents from one language to another  Text summarization Note: Not all such applications require NLP Keyword based techniques can used for identifying particular subject areas, e.g. legal, financial, etc.

32 Natural language Processing (Heshaam Feili – July 2003) 32 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.

33 Natural language Processing (Heshaam Feili – July 2003) NL- Understanding  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.

34 Natural language Processing (Heshaam Feili – July 2003) 34 Dialogue-based Applications Dialogue-based applications involve man-machine communication  NL database query systems  Automated customer services, e.g. banking services

35 Natural language Processing (Heshaam Feili – July 2003) 35 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

36 Natural language Processing (Heshaam Feili – July 2003) ?


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