Natural Language Processing Heshaam Feili hfaili July 2003.

Natural Language Processing

Heshaam Feilihttp://mehr.sharif.edu/~hfaili

July 2003

Natural language Processing (Heshaam Feili – July 2003) 2

Session Agenda

Artificial Intelligence

Natural Language Processing

History of NLP

Applications of NLP


AI Concepts and Definitions

Encompasses Many Definitions AI Involves Studying Human

Thought Processes Representing Thought

Processes on Machines


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)


AI Objectives

Make machines smarter (primary goal)

Understand what intelligence is (Nobel Laureate purpose)

Make machines more useful (practical purpose)

(Winston and Prendergast [1984])


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


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


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


Symbolic Processing

Use Symbols to Represent Problem Concepts

Apply Various Strategies and

Rules to Manipulate these Concepts


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


Symbol Structures (Relationships)

(DEFECTIVE product) (EQUAL (LIABILITY defendant)

0.8) tastes_good (chocolate).


AI Programs Manipulate Symbols to Solve Problems

Symbols and Symbol Structures Form Knowledge Representation

Artificial Intelligence Dealings Primarily with Symbolic, Nonalgorithmic Problem- Solving Methods


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


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)


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


Additional AI Areas

News Summarization Language Translation Fuzzy Logic Genetic Algorithms Intelligent Software Agents

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.


Major Disciplines Studying LanguageDiscipline Typical 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?


Interaction Level

The level that computer and human interact.

NL used for make Interaction level near to human.

Human Computer

Command-lineNL UIGraphical UI

Interaction level


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.


Other Titles

The most common titles, apart from Natural Language Processing include:

Automatic Language Processing Computational Linguistics Natural Language Understanding


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.


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.


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.


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).


NLP History (1)

The first recognizable NLP application was a dictionary look-up system developed at Birkbeck College, London in 1948.

NLP from 1966-1980 Augmented Transition Networks Case Grammar


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.


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 1980 - 1990

- Grammar Formalisms NLP from 1990- now

- Multilinguality and Multimodality


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


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.


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.


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.


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.


Dialogue-based Applications

Dialogue-based applications involve man-machine communication

NL database query systems

Automated customer services, e.g. banking services


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


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Natural Language Processing Heshaam Feili hfaili July 2003.

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