Artificial Intelligence Unit VI: NLP & Expert System

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Natural Language Processing and Expert System


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Artificial Intelligence: Natural Language Processing and Expert Systems UNIT VI

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Artificial intelligence Robotics Vision systems Learning systems Natural language processing Neural networks Expert systems

Artificial Intelligence:

Artificial Intelligence The branch of computer science concerned with making computers behave like humans. Artificial intelligence includes : games playing : programming computers to play games such as chess and checkers expert systems : programming computers to make decisions in real-life situations (for example, some expert systems help doctors diagnose diseases based on symptoms) natural language : programming computers to understand natural human languages neural networks : Systems that simulate intelligence by attempting to reproduce the types of physical connections that occur in animal brains robotics : programming computers to see, hear and react to other sensory stimuli

Overview of Expert Systems:

Overview of Expert Systems Can… Explain their reasoning or suggested decisions Display intelligent behavior Draw conclusions from complex relationships Provide portable knowledge Expert system shell A collection of software packages and tools used to develop expert systems

Limitations of Expert Systems:

Limitations of Expert Systems Not widely used or tested Limited to relatively narrow problems Possibility of error Cannot refine own knowledge base Difficult to maintain May have high development costs Raise legal and ethical concerns

Capabilities of Expert Systems:

Capabilities of Expert Systems Strategic goal setting Decision making Planning Design Quality control and monitoring Diagnosis Explore impact of strategic goals Impact of plans on resources Integrate general design principles and manufacturing limitations Provide advise on decisions Monitor quality and assist in finding solutions Look for causes and suggest solutions

When to Use an Expert System`:

When to Use an Expert System` Capture and preserve irreplaceable human expertise Provide expertise needed at a number of locations at the same time or in a hostile environment that is dangerous to human health Provide expertise that is expensive or rare Develop a solution faster than human experts can Provide expertise needed for training and development

Components of an Expert System:

Components of an Expert System Knowledge base Stores all relevant information, data, rules, cases, and relationships used by the expert system Inference engine Seeks information and relationships from the knowledge base and provides answers, predictions, and suggestions in the way a human expert would Rule A conditional statement that links given conditions to actions or outcomes

Components of an Expert System:

Components of an Expert System Fuzzy logic Backward chaining A method of reasoning that starts with conclusions and works backward to the supporting facts Forward chaining A method of reasoning that starts with the facts and works forward to the conclusions

PowerPoint Presentation:

Inference engine Explanation facility Knowledge base acquisition facility User interface Knowledge base Experts User

Explanation Facility:

Explanation Facility Explanation facility A part of the expert system that allows a user or decision maker to understand how the expert system arrived at certain conclusions or results

Knowledge Acquisition Facility :

Knowledge Acquisition Facility Knowledge acquisition facility Provides a convenient and efficient means of capturing and storing all components of the knowledge base Knowledge base Knowledge acquisition facility Expert

Expert Systems Development :

Determining requirements Identifying experts Construct expert system components Implementing results Maintaining and reviewing system Expert Systems Development Domain The area of knowledge addressed by the expert system.

Participants in Expert Systems Development and Use :

Participants in Expert Systems Development and Use Domain expert The individual or group whose expertise and knowledge is captured for use in an expert system Knowledge user The individual or group who uses and benefits from the expert system Knowledge engineer Someone trained or experienced in the design, development, implementation, and maintenance of an expert system Schematic

PowerPoint Presentation:

Expert system Domain expert Knowledge engineer Knowledge user

Rules for a Credit Application :

Rules for a Credit Application Mortgage application for a loan for $100,000 to $200,000 If there are no previous credits problems, and If month net income is greater than 4x monthly loan payment, and If down payment is 15% of total value of property, and If net income of borrower is > $25,000, and If employment is > 3 years at same company Then accept the applications Else check other credit rules

Evolution of Expert Systems Software :

Evolution of Expert Systems Software Expert system shell Collection of software packages & tools to design, develop, implement, and maintain expert systems Ease of use low high Before 1980 1980s 1990s Traditional programming languages Special and 4 th generation languages Expert system shells

Advantages of Expert Systems:

Advantages of Expert Systems Easy to develop and modify The use of heuristics Development by knowledge engineers and users

Expert Systems Development Alternatives :

Expert Systems Development Alternatives low high low high Development costs Time to develop expert system Use existing package Develop from shell Develop from scratch

Applications of Expert Systems and Artificial Intelligence:

Applications of Expert Systems and Artificial Intelligence Credit granting Information management and retrieval AI and expert systems embedded in products Plant layout Hospitals and medical facilities Help desks and assistance Employee performance evaluation Loan analysis Virus detection Repair and maintenance Shipping Marketing Warehouse optimization

Natural Language Processing:

Natural Language Processing

Natural Language Processing:

Natural Language Processing NLP problem can be divided into two tasks: Processing written text, using lexical, syntactic and semantic knowledge of the language as well as the required real world information. Processing spoken language, using all the information needed above plus additional knowledge about phonology as well as enough added information to handle the further ambiguities that arise in speech.

Natural Language Processing:

There are two components of NLP. Natural Language Understanding : Mapping the given input in the natural language into a useful representation. morphological analysis, syntactic analysis, semantic analysis, discourse analysis, … Natural Language Generation : Producing output in the natural language from some internal representation. deep planning syntactic generation Natural Language Processing

Steps in NLP:

Steps in NLP Morphological Analysis : Individual words are analyzed into their components and non-word tokens such as punctuation are separated from the words. Syntactic Analysis: Linear sequences of words are transformed into structures that show how the words relate to each other. Semantic Analysis : The structures created by the syntactic analyzer are assigned meanings. Discourse integration : The meaning of an individual sentence may depend on the sentences that precede it and may influence the meanings of the sentences that follow it. Pragmatic Analysis: The structure representing what was said is reinterpreted to determine what was actually meant.

PowerPoint Presentation:

The steps in natural language understanding are as follows: Words Morphological Analysis Morphologically analyzed words Syntactic Analysis Syntactic Structure Semantic Analysis Context-independent meaning representation Discourse Processing Final meaning representation

Morphological Analysis:

Morphological Analysis Suppose we have an english interface to an operating system and the following sentence is typed: I want to print Bill’s.init file. Morphological analysis must do the following things: Pull apart the word “Bill’s” into proper noun “Bill” and the possessive suffix “’s” Recognize the sequence “.init” as a file extension that is functioning as an adjective in the sentence.

Syntactic Analysis:

Syntactic Analysis Parsing -- converting a flat input sentence into a hierarchical structure that corresponds to the units of meaning in the sentence. There are different parsing formalisms and algorithms. Most formalisms have two main components: – grammar -- a declarative representation describing the syntactic structure of sentences in the language. – parser -- an algorithm that analyzes the input and outputs

A parse tree :

A parse tree John ate the apple. S -> NP VP VP -> V NP NP -> NAME NP -> ART N NAME -> John V -> ate ART-> the N -> apple S NP VP NAME John V ate NP ART N the apple

Exercise: For each of the following sentences, draw a parse tree:

Exercise: For each of the following sentences, draw a parse tree John wanted to go to the movie with Sally I heard the story listening to the radio. All books and magazines that deal with controversial topics have been removed from the shelves.

Grammars and Parsers:

Grammars and Parsers Grammar formalism such as this one underlie many linguistic theories, which in turn provide the basis for many natural language understanding systems. Pure context free grammars are not effective for describing natural languages. NLPs have less in common with computer language processing systems such as compilers. Parsing process takes the rules of the grammar and compares them against the input sentence. The simplest structure to build is a Parse Tree, which simply records the rules and how they are matched. Every node of the parse tree corresponds either to an input word or to a nonterminal in our grammar. Each level in the parse tree corresponds to the application of one grammar rule.

Semantic Analysis:

Semantic Analysis Semantic analysis must do two important things: It must map individual words into appropriate objects in the knowledge base It must create the correct structures to correspond to the way the meanings of the individual words combine with each other.

Discourse Integration:

Discourse Integration Discourses are collection of coherent sentences – Mary bought a book for Kelly. She didn’t like it. She refers to Mary or Kelly. -- possibly Kelly It refers to what -- book. – Mary had to lie for Kelly. She didn’t like it.

Pragmatic Analysis:

Pragmatic Analysis The final step toward effective understanding is to decide what to do as a results. One possible thing to do is to record what was said as a fact and be done with it. For some sentences, whose intended effect is clearly declarative, that is precisely correct thing to do. But for other sentences, including ths one, the intended effect is different. We can discover this intended effect by applyling a set of rules that characterize cooperative dialogues. The final step in pragmatic processing is to translate, from the knowledge based representation to a command to be executed by the system. The results of the understanding process is Lpr /wsmith/stuff.init

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