Materi Pendukung : T0264P21_2
Natural Language Processing
Progress on building computer
systems that process natural
language in any meaningful sense
requires considering language as part
of a larger communicative situation.
Regarding language as
communication requires consideration
of what is said (literally), what is
intended, and the relationship
between the two.
-Barbara Grosz, Utterance and
Objective
The value to our society of being able to communicate with
computers in everyday "natural" language cannot be overstated.
Imagine asking your computer "Does this candidate have a good
record on the environment?" or "When is the next televised National
League baseball game?" Or being able to tell your PC "Please
format my homework the way my English professor likes it."
Commercial products can already do some of these things, and AI
scientists expect many more in the next decade. One goal of AI work
in natural language is to enable communication between people and
computers without resorting to memorization of complex commands
and procedures. Automatic translation---enabling scientists,
business people and just plain folks to interact easily with people
around the world---is another goal. Both are just part of the broad
field of AI and natural language, along with the cognitive science
aspect of using computers to study how humans understand
language.
Good Places to Start
What is Computational Linguistics? Hans Uszkoreit, CL Department,
University of the Saarland, Germany. 2000. A short, non-technical
overview this exciting field
Speech and Language Processing: An Introduction to Natural
Language Processing, Computational Linguistics, and Speech
Recognition. By Daniel Jurafsky and James H. Martin. Prentice-Hall,
2000. Both the Preface and Chaper 1 are available online as are the
resources for all of the chapters.
Natural Language. A summary by Patrick Doyle. Very informative,
though there are some spots that are quite technical.
NLP Tutorials. From Dave Inman, School of Computing, South Bank
University, London. The topics covered include: Can computers
understand language?; What kinds of ambiguity exist and why does
ambiguity hinder NLP?; and A simple Prolog parser to analyse the
structure of language.
Natural Language Processing. Lecture Notes from Associate
Professor John Batali's course in Artificial Intelligence Modeling at
the University of California at San Diego's Department of Cognitive
Science.
Glossary of Linguistic Terms. Compiled by Dr. Peter Coxhead of The
University of Birmingham School of Computer Science for his
students.
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Also see his Introduction to NLP.
The Futurist - The Intelligent Internet. The Promise of Smart
Computers and E-Commerce. By William E. Halal. Government
Computer News Daily News (June 23, 2004). "Scientific advances
are making it possible for people to talk to smart computers, while
more enterprises are exploiting the commercial potential of the
Internet. ... [F]orecasts conducted under the TechCast Project at
George Washington University indicate that 20 commercial aspects
of Internet use should reach 30% 'take-off' adoption levels during the
second half of this decade to rejuvenate the economy. Meanwhile,
the project's technology scanning finds that advances in speech
recognition, artificial intelligence, powerful computers, virtual
environments, and flat wall monitors are producing a 'conversational'
human-machine interface. These powerful trends will drive the next
generation of information technology into the mainstream by about
2010. ... The following are a few of the advances in speech
recognition, artificial intelligence, powerful chips, virtual
environments, and flat-screen wall monitors that are likely to produce
this intelligent interface. ... IBM has a Super Human Speech
Recognition Program to greatly improve accuracy, and in the next
decade Microsoft's program is expected to reduce the error rate of
speech recognition, matching human capabilities. ... MIT is planning
to demonstrate their Project Oxygen, which features a voicemachine interface. ... Amtrak, Wells Fargo, Land's End, and many
other organizations are replacing keypad-menu call centers with
speech-recognition systems because they improve customer service
and recover investment in a year or two. ... General Motors OnStar
driver assistance system relies primarily on voice commands, with
live staff for backup; the number of subscribers has grown from
200,000 to 2 million and is expected to increase by 1 million per
year. The Lexus DVD Navigation System responds to over 100
commands and guides the driver with voice and visual directions."
Experts Use AI to Help GIs Learn Arabic. By Eric Mankin. USC
News (June 21, 2004). " To teach soldiers basic Arabic quickly, USC
computer scientists are developing a system that merges artificial
intelligence with computer game techniques. The Rapid Tactical
Language Training System, created by the USC Viterbi School of
Engineering's Center for Research in Technology for Education
(CARTE) and partners, tests soldier students with videogame
missions in animated virtual environments where, to pass, the
students must successfully phrase questions and understand
answers in Arabic." Read the story and then watch the video!
Natural Language Processing: She Needs Something Old &
Something New (maybe something borrowed and something blue,
too.) Karen Sparck Jones, University of Cambridge, UK. Her 1994
Presidential Address to the Assn. for Computational Linguistics
(ACL). "I want to assess where we are now, in computational
linguistics and natural language processing, compared with where
we started, and to put my view of what we need to do next. ...
Computational linguistics, or natural language processing (NLP), is
nearly as old as serious computing. Work began more than forty
years ago, and one can see it going through successive phases...."
Natural Language Processing FAQ. Maintained by Dragomir R.
Radev. Dept. of Computer Science, Columbia University.
An Overview of Empirical Natural Language Processing. By Eric Brill
and Raymond J. Mooney (1997). AI Magazine 18 (4): 13 - 24. "In
recent years, there has been a resurgence in research on empirical
methods in natural language processing. These methods employ
learning techniques to automatically extract linguistic knowledge
from natural language corpora rather than require the system
developer to manually encode the requisite knowledge. The current
special issue reviews recent research in empirical methods in
speech recognition, syntactic parsing, semantic processing,
information extraction, and machine translation. This article presents
an introduction to the series of specialized articles on these topics
and attempts to describe and explain the growing interest in using
learning methods to aid the development of natural language
processing systems."
Chatbots / Chatterbots:
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Alice, the Chat Robot. Winner of the 2000, 2001 & 2004
Loebner Prize.
Brian is a computer program that thinks it's an 18 year old
college student. It was written as an entry in the 1998
Loebner Competition, where it won third place out of six
entries. From Joe Strout.
Chatter Bots. From BotSpot.
Chatterbots. Hosted by Simon Laven. An extensive
collection that includes classic chatterbots, complex
chatterbots, friendly chatterbots, non-English bots (German,
Spanish, French, and other languages), and much more.
Chatterbot links from the Open Directory project.
Deepti -- A Hindi Speaking Chat Robot. Be sure to see our
news archive for the BBC article, Hindi chatbot breaks new
ground (August 27, 2002).
Eliza - "a friend you could never have before". One of the
earliest chatterbots.
o Also see: Eliza Doolittle on our Namesakes page.
George - winner of the 2005 Loebner Prize. [See these
related articles.]
Jabberwacky. As stated on the About Jabberwacky page, it
"is an artificial intelligence - a chat robot, often known as a
'chatbot' or 'chatterbot'. It aims to simulate natural human
chat in an interesting, entertaining and humorous manner.
Jabberwacky is different. It learns. In some ways it models
the way humans learn language, facts, context and rules."
[Also see this related article.]
"John Lennon Artificial Intelligence Project (JLAIP) is
recreating the personality of the late Beatle, John Lennon,
by programming an Artificial Intelligence (AI) engine with
Lennon's own words and thoughts. Triumph PC's
breakthroughs take the field of AI to an entirely new level,
thus making Persona-Bots™ (robots inhabited with unique
and authentic human personalities) possible and further
blurring the precarious line between man and machine."
(Also see this related article.)
Mr. Mind: "Turning the Turing Test upside down, MRMIND
challenges you to prove to him that you are human. Can you
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claim that your 'human' attributes will forever be exclusively
human? The Blurring Test is about human progress:
Someday it might be important to convince our computers
(and each other) that we are human."
"Pandorabots.com is a software robot (also known as a bot)
hosting service. From any browser, you may create and
publish your own robots to anyone via the web. We believe
that our technology yields the fastest bots available on the
Internet. The bots are based on AIML and spring entirely
from the work of Dr. Richard Wallace and the A.L.I.C.E. and
AIML free software community based at www.AliceBot.Org."
As of this posting (8/02), there is no charge to create your
own Chat Bot.
The Personality Forge. "Come on in, and chat with bots and
botmasters, then create your own artificial intelligence
personality, and turn it loose to chat with both real people
and other chat bots. Here you'll find thousands of AI
personalities...." Maintained by Benji Adams.
Valerie and Marion (Tank) LeFleur, the roboceptionists from
the lobby of Carnerie Mellon University's Newell-Simon Hall.
[See this related article.]
dialogues with colorful personalities of early ai
Why did the chicken cross the road? See our NewsToon!
To find out who coined the term, chatterbot, see our
Namesakes page (which is where you'll also meet the man
who designed the Turing Test).
Whatever happened to machines that think? By Justin Mullins.
New Scientist (April 23, 2005; Issue 2496: pages 32 - 37). "Clever
computers are everywhere. From robotic lawnmowers to intelligent
lighting, washing machines and even car engines that selfdiagnose faults, there's a silicon brain in just about every modern
device you can think of. But can you honestly call any machine
intelligent in a meaningful sense of the word? One rainy afternoon
last February I decided to find out. I switched on the computer in
my study, and logged on to www.intellibuddy.com, home to one of
the leading artificial intelligences on the planet, to see what the
state-of-the-art has to offer. ..."
Readings Online
AI in the news: Natural Language Processing
"Computational Linguistics is the only publication devoted
exclusively to the design and analysis of natural language
processing systems. From this unique quarterly, university and
industry linguists, computational linguists, artificial intelligence (AI)
investigators, cognitive scientists, speech specialists, and
philosophers get information about computational aspects of
research on language, linguistics, and the psychology of language
processing and performance. Published by The MIT Press for: The
Association for Computational Linguistics." Abstracts are available
online.
Natural Language Understanding. By Avron Barr (1980). AI
Magazine 1(1): 5-10. "This is an excerpt from the Handbook of
Artificial Intelligence, a compendium of hundreds of articles about AI
ideas, techniques, and programs being prepared at Stanford
University by AI researchers and students from across the country."
Don't miss the fascinating section: Early History.
Empirical Methods in Information Extraction. By Claire Cardie
(1997). AI Magazine 18 (4): 65-79. "This article surveys the use of
empirical, machine-learning methods for a particular natural
language-understanding task-information extraction. The author
presents a generic architecture for information-extraction systems
and then surveys the learning algorithms that have been developed
to address the problems of accuracy, portability, and knowledge
acquisition for each component of the architecture."
Duo-Mining - Combining Data and Text Mining. By Guy Creese.
DMReview.com (September 16, 2004). "As standalone capabilities,
the pattern-finding technologies of data mining and text mining have
been around for years. However, it is only recently that enterprises
have started to use the two in tandem - and have discovered that it
is a combination that is worth more than the sum of its parts. First of
all, what are data mining and text mining? They are similar in that
they both 'mine' large amounts of data, looking for meaningful
patterns. However, what they analyze is quite different. ...
Collections and recovery departments in banks and credit card
companies have used duo-mining to good effect. Using data mining
to look at repayment trends, these enterprises have a good idea on
who is going to default on a loan, for example. When logs from the
collection agents are added to the mix, the understanding gets even
better. For example, text mining can understand the difference in
intent between, 'I will pay,' 'I won't pay,' 'I paid' and generate a
propensity to pay score - which, in turn, can be data mined. To take
another example, if a customer says, 'I can't pay because a tree fell
on my house;' all of a sudden it is clear that it's not a 'bad'
delinquency - but rather a sales opportunity for a home loan."
A Short Introduction to Text-to-Speech Synthesis. By Thierry Dutoit.
The Circuit Theory and Signal Processing Lab of the Faculte
Polytechnique de Mons. "I try to give here a short but
comprehensive introduction to state-of-the-art Text-To-Speech (TTS)
synthesis by highlighting its Digital Signal Processing (DSP) and
Natural Language Processing (NLP) components."
At I.B.M., That Google Thing Is So Yesterday. By James Fallows.
The New York Times (December 26, 2004; reg. req'd.). "Suddenly,
the computer world is interesting again. ... The most attractive
offerings are free, and they are concentrated in the newly sexy field
of 'search.' ... [T]oday's subject is the virtually unpublicized search
strategy of another industry heavyweight: I.B.M. ... I.B.M. says that
its tools will make possible a further search approach, that of
'discovery systems' that will extract the underlying meaning from
stored material no matter how it is structured (databases, e-mail
files, audio recordings, pictures or video files) or even what language
it is in. The specific means for doing so involve steps that will raise
suspicions among many computer veterans. These include 'natural
language processing,' computerized translation of foreign languages
and other efforts that have broken the hearts of artificial-intelligence
researchers through the years. But the combination of ever-faster
computers and ever-evolving programming allowed the systems I
saw to succeed at tasks that have beaten their predecessors. ... ...
Jennifer Chu-Carroll of I.B.M. demonstrated a system called Piquant,
which analyzed the semantic structure of a passage and therefore
exposed 'knowledge' that wasn't explicitly there. After scanning a
news article about Canadian politics, the system responded correctly
to the question, 'Who is Canada's prime minister?' even though
those exact words didn't appear in the article."
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Check out IBM's Natural Language Processing projects and
their Natural Language Processing research overview.
dialogues with colorful personalities of early ai. By Guven Guzeldere
and Stefano Franchi. (1995). From Constructions of the Mind:
Artificial Intelligence and the Humanities, a special issue of the
Stanford Humanities Review, Volume 4,Issue 2. "Of all the legacies
of the era of the sixties, three colorful, not to say garrulous,
"personalities" that emerged from the early days of artificial
intelligence research are worth mentioning: ELIZA, the Rogerian
psychotherapist; PARRY, the paranoid; and (as part of a younger
generation) RACTER, the "artificially insane" raconteur. All three of
these "characters" are natural language processing systems that can
"converse" with human beings (or with one another) in English.
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Meet our collection of chatterbots!
LifeCode: A Deployed Application for Automated Medical Coding. By
Daniel T. Heinze, Mark Morsch, Ronald Sheffer, Michelle Jimmink,
Mark Jennings, William Morris, and Amy Morsch. AI Magazine 22(2):
76-88 (Summer 2001). This paper is based on the authors'
presentation at the Twelfth Innovative Applications of Artificial
Intelligence Conference (IAAI-2000). "LifeCode is a natural language
processing (NLP) and expert system that extracts demographic and
clinical information from free-text clinical records."
Natural Language Processing and Knowledge Representation:
Language for Knowledge and Knowledge for Language. Edited by
Lucja M. Iwanska and Stuart C. Shapiro. AAAI Press. The following
excerpt is from the Preface which is available online: "The research
direction of natural language-based knowledge representation and
reasoning systems constitutes a tremendous change in how we view
the role of natural language in an intelligent computer system. The
traditional view, widely held within the artificial intelligence and
computational linguistics communities, considers natural language
as an interface or front end to a system such as an expert system or
knowledge base. In this view, inferencing and other interesting
information and knowledge processing tasks are not part of natural
language processing. By contrast, the computational models of
natural language presented in this book view natural language as a
knowledge representation and reasoning system with its own
unique, computationally attractive representational and inferential
machinery. This new perspective sheds some light on the actual, still
largely unknown, relationship between natural language and the
human mind. Taken to an extreme, such approaches speculate that
the structure of the human mind is close to natural language. In
other words, natural language is essentially the language of human
thought."
"I'm sorry Dave, I'm afraid I can't do that": Linguistics, Statistics, and
Natural Language Processing circa 2001. By Lillian Lee, Cornell
Natural Language Processing Group. To appear in the National
Academies' Study on Fundamentals of Computer Science. "A brief,
general-audience overview of the history of natural language
processing, focusing on data-driven approaches."
A Performance Evaluation of Text-Analysis Technologies. By Wendy
Lehnert and Beth Sundheim (1991). AI Magazine 12 (3): 81-94. "A
performance evaluation of 15 text-analysis systems conducted to
assess the state of the art for detailed information extraction from
unconstrained continuous text. ... Based on multiple strategies for
computing each metric, the competing systems were evaluated for
recall, precision, and overgeneration. The results support the claim
that systems incorporating natural language-processing techniques
are more effective than systems based on stochastic techniques
alone."
Natural Language Lecture Slides & Accompanying Transcripts from
Professors Tomás Lozano-Pérez & Leslie Kaelbling's Spring 2003
course: Artificial Intelligence. Available from MIT OpenCourseWare.
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Also available from OpenCourseWare: 6.881 Natural
Language Processing (Fall 2004), Professor Regina
Barzilay's graduate level introducory course.
Natural Language Understanding and Semantics. Section 1.2.4 of
Chapter One (available online) of George F. Luger's textbook,
Artificial Intelligence: Structures and Strategies for Complex Problem
Solving, 5th Edition (Addison-Wesley; 2005). "One of the longstanding goals of artificial intelligence is the creation of programs
that are capable of understanding and generating human language.
Not only does the ability to use and understand natural language
seem to be a fundamental aspect of human intelligence, but also its
successful automation would have an incredible impact on the
usability and effectiveness of computers themselves. ...
Understanding natural language involves much more than parsing
sentences into their individual parts of speech and looking those
words up in a dictionary. Real understanding depends on extensive
background knowledge about the domain of discourse and the
idioms used in that domain as well as an ability to apply general
contextual knowledge to resolve the omissions and ambiguities that
are a normal part of human speech."
Computers That Speak Your Language - Voice recognition that
finally holds up its end of a conversation is revolutionizing customer
service. Now the goal is to make natural language the way to find
any type of information, anywhere. By Wade Roush. Technology
Review (June 2003). "Building a truly interactive customer service
system like Nuance’s requires solutions to each of the major
challenges in natural-language processing: accurately transforming
human speech into machine-readable text; analyzing the text’s
vocabulary and structure to extract meaning; generating a sensible
response; and replying in a human-sounding voice." And be sure to
see the illustration in the article: Inside a Conversational Computer.
Chatbot bids to fool humans - A computer program designed to talk
like a human is preparing for its biggest test in its bid to be truly
"intelligent". By Jo Twist. BBC (September 22, 2003). "Jabberwacky
lives on a computer hard drive, tells jokes, uses slang, sometimes
swears and can be quite a confrontational conversationalist. What
sets this chatty AI (artificial intelligence) chatbot apart from others is
the more it natters, the more it learns. The bot is the only UK finalist
in this year's Loebner Prize and is hoping to chat its way to a gold
medal for its creator, Rollo Carpenter. The Loebner Prize is the
annual competition to find the computer with the most convincing
conversational skills and started in 1990. Jabberwacky will join eight
other international finalists in October, when they pit their wits
against flesh and blood judges to see if they can pass as one of
them. It is the ultimate Turing Test, which was designed by
mathematician Alan Turing to see whether computers 'think' and
have 'intelligence'."
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Meet our collection of chatterbots!
Go to our Turing Test page.
Related Web Sites
The Association for Computational Linguistics (ACL) is the
"international scientific and professional society for people working
on problems involving natural language and computation."
ACL NLP/CL Universe. Choose "Browse" to see menus of what is
offered (introductory materials, research groups, conferences,
bibliographies, etc.) or choose "Search" for a keyword search
engine. ["The NLP/CL Universe is a Web catalog/search engine that
is devoted to Natural Language Processing and Computational
Linguistics Web sites. It exists since March 18, 1995." Maintained by
Dragomir R. Radev for ACL.]
AI on the Web: Natural Language Processing. A resource
companion to Stuart Russell and Peter Norvig's "Artificial
Intelligence: A Modern Approach" with links to reference material,
people, research groups, books, companies and much more.
National Centre for Text Mining (NaCTeM): "We provide text mining
services in response to the requirements of the UK academic
community. Our initial focus is on applications in the biological and
medical domains, where the major successes in the mining of
scientific texts have so far occurred. We also make significant
contributions to the text mining research community, both nationally
and internationally."
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Also see:
o their FAQ, "What is text mining? - Text mining attempts
to discover new, previously unknown information by
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applying techniques from information retrieval, natural
language processing and data mining. ..."
this related article: Digging for data that can change
our world.
Natural Language Group. Information Sciences Institute, University
of Southern California. Be sure to see What It's All About: "Natural
Language Processing (or Human Language Technology, or
Computational Linguistics) is about the treatment of human
languages by computer dating since the early 1950s. NLP has
experienced unprecedented growth over the past few years. ..."
Natural Language Learning at UT Austin. "Natural language
processing systems are difficult to build, and machine learning
methods can help automate their construction significantly. Our
research in learning for natural language mainly involves applying
inductive logic programming and other relational learning techniques
to constructing database interfaces and information extraction
systems from supervised examples. However, we have also
conducted research in learning for syntactic parsing, machine
translation, word-sense disambiguation, and morphology (past tense
generation)." Check out the 3 demos of learning natural-language
interfaces: Geoquery; RestaurantQuery; and JobQuery.
Natural Language Processing Course Listing, part of the 2004 NLP
Course Survey conducted by ACL (Association for Computational
Linguistics).
The Natural Language Processing Dictionary (NLP Dictionary).
Compiled by Bill Wilson, Associate Professor in the Artificial
Intelligence Group, School of Computer Science and Engineering,
University of NSW. "You should use The NLP Dictionary to clarify or
revise concepts that you have already met. The NLP Dictionary is
not a suitable way to begin to learn about NLP."
Natural Language Processing Group. Department of Artificial
Intelligence, University of Edinburgh.
"The goal of the [Microsoft] Natural Language Processing (NLP)
group is to design and build a computer system that will analyze,
understand, and generate languages that humans use naturally, so
that eventually you can address your computer as though you were
addressing another person. This goal is not easy to reach. ... The
challenges we face stem from the highly ambiguous nature of
natural language."
Natural Language Processing Resource Sites. from Mary D. Taffet.
"Please note: This webpage was created primarily for the use of the
students in the Natural Language Processing course taught by
Elizabeth Liddy at Syracuse University's School of Information
Studies. Students of other NLP or Computational Linguistics courses
are more than welcome to make use of this page as well. The
primary purpose of this page is to point to: 1.NLP-related demos that
are available online ... 2.Resources relevant to the various levels of
language processing 3.Other useful links for NLP students, relating
to any aspect of Natural Language Processing that might be
encountered in an academic course, from the lowest levels of
language processing to the highest levels."
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She also offers an instructor version: "This page for
instructors differs from the student version by also including
links to online course descriptions and syllabi and online
degree program descriptions."
Natural Language Program. Artificial Intelligence Center, SRI. "The
SRI AI Center Natural Language Program does research on natural
language processing theory and applications. The Program has
three subgroups. Multimedia/Multimodal Interfaces ... Spoken
Language Systems ... Written Language Systems." Be sure to follow
their links to projects, applications, and more!
"The Natural Language Software Registry (NLSR) [fourth edition] is
a concise summary of the capabilities and sources of a large amount
of natural language processing (NLP) software available to the NLP
community. It comprises academic, commercial and proprietary
software with specifications and terms on which it can be acquired
clearly indicated." From the Language Technology Lab of the
German Research Centre for Artificial Intelligence (DFKI GmbH).
START. "The START Natural Language System is a software
system designed to answer questions that are posed to it in natural
language. START parses incoming questions, matches the queries
created from the parse trees against its knowledge base and
presents the appropriate information segments to the user. In this
way, START provides untrained users with speedy access to
knowledge that in many cases would take an expert some time to
find."
Stanford NLP Group. "A distinguishing feature of the Stanford NLP
Group is our effective combination of sophisticated and deep
linguistic modeling and data analysis with innovative probabilistic
and machine learning approaches to NLP."
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Be sure to see Christopher Manning's annotated list of
statistical natural language processing and corpus-based
computational linguistics resources.
Related Pages
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Discourse Analysis
Ethics
General Index to AI in the news: Natural Language
Processing
Human-Computer Interfaces
Machine Translation
Marketing, Customer Relations & E-Commerce
Natural Language Understanding & Generation
Philosophy
Software & Hardware
Speech
Turing Test
and check out our page of Namesakes to find discover what
contribution George Kingsley Zipf (1902-1950) made to NLP
More Readings
Aikins, Janice, Rodney Brooks, William Clancey, et al. 1981. Natural
Language Processing Systems. In The Handbook of Artificial
Intelligence, Vol. I, ed.Barr, Avron and Edward A. Feigenbaum, 283321. Stanford/Los Altos, CA: HeurisTech Press/William Kaufmann,
Inc.
Allen, J. F. 1994. Natural Language Understanding. Redwood City,
CA: Benjamin/Cummings. A new edition of a classic work.
Bobrow, Daniel. 1968. Natural Language Input for a Computer
Problem Solving System. In Semantic Information Processing, ed.
Minsky, Marvin, 133-215. Cambridge, MA: MIT Press.
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Also see Daniel Bobrow's 1964 Doctoral Thesis: Natural
Language Input for a Computer Problem Solving System. As
stated in the Abstract: "The STUDENT problem solving
system, programmed in LISP, accepts as input a comfortable
but restricted subset of English which can express a wide
variety of algebra story problems. STUDENT finds the
solution to a large class of these problems. STUDENT can
utilize a store of global information not specific to any one
problem, and may make assumptions about the interpretation
of ambiguities in the wording of the problem being solved."
Charniak, E. 1993. Statistical Language Learning. Cambridge, MA:
MIT Press.
Cohen, P., J. Morgan, and M. Pollack. 1990. Intentions in
Communication. Cambridge, MA: MIT Press.
Grosz, Barbara J., Martha E. Pollack, and Candace L. Sidner. 1989.
Discourse. In Foundations of Cognitive Science, ed. Posner, M.,
437-468. Cambridge, MA: MIT Press.
Grosz, Barbara J., Karen Sparck Jones, and Bonnie L. Webber,
editors. 1986. Readings in Natural Language Processing. San
Mateo, CA: Morgan Kaufmann.
Mahesh, Kavi, and Sergei Nirenburg. 1997. Knowledge-Based
Systems for Natural Language. In The Computer Science and
Engineering Handbook, ed. Allen B. Tucker, Jr., 637-653. Boca
Raton, FL: CRC Press, Inc.
McKeown, K., and W. Swartout. 1987. Language Generation and
Explanation. In Annual Review of Computer Science, Vol. 2, Palo
Alto, CA: Annual Reviews.
Patterson, Dan W. 1990. Natural Language Processing. In
Introduction to Artificial Intelligence and Expert Systems by Dan W.
Patterson, 227-270. Englewood Cliffs, NJ: Prentice Hall.
Shank, Roger C. 1975. The Structure of Episodes in Memory. In
Computation and Intelligence: Collected Readings, ed. Luger,
George F., 236-259. Menlo Park/Cambridge, MA: AAAI Press/The
MIT Press, 1995.
Weizenbaum, J. 1965. ELIZA--A Computer Program for the Study of
Natural Language Communication Between Man and Machine.
Communications of the ACM, 9 (1): 36-45. A pioneering work.
Winograd, T. 1972. Understanding Natural Language. New York:
Academic Press. A pioneering work.