article

Free access

Knowledge and natural language processing

Authors:

Jim Barnett,

Kevin Knight,

Inderjeet Mani,

Elaine RichAuthors Info & Claims

Communications of the ACM, Volume 33, Issue 8

Pages 50 - 71

https://doi.org/10.1145/79173.79177

Published: 01 August 1990 Publication History

PDF eReader

Abstract

KBNL is a knowledge-based natural language processing system that is novel in several ways, including the clean separation it enforces between linguistic knowledge and world knowledge, and its use of knowledge to aid in lexical acquisition. Applications of KBNL include intelligent interfaces, text retrieval, and machine translation.

References

[1]

Ades, A. and Steedman, M. On the Order of Words. Linguistics and Philosophy 4 (1982), 5!7-558.]]

Google Scholar

[2]

Appelt, D.E. Bidirectional Gr'ammars and the Design of Natural Language Generation Systems. TINLAP-3 Position Papers, 1987.]]

Digital Library

Google Scholar

[3]

Arnold, D.J., Krauwer, S., Rosner, M., des Tombe, L. and Varile, G.B. The,(C,A 2>,T Framework in Eurotra: A Theoretically Committed Notation for MT. In Proceedings of the llth International Conference on Computational Linguistics (COLING), 1986.]]

Digital Library

Google Scholar

[4]

Ballard, B. and Stumberger, D.E. Semantic Acquisition in TELI: A Transportable, User- Customized Natural Language Processor. In Proceedings of the 24th Annual Meeting of the Association for Computational Linguistics. (1986).]]

Digital Library

Google Scholar

[5]

Barnett, J. and Mani, I. Using Bidirectional Semantic Rules for Generation. In Proceedings of the Fifth International Workshop on Natural Language Generation (1990).]]

Google Scholar

[6]

Bennett, W.S. and Slocum, J. The LRC Machine Translation System. Comput. Linguist. 11, 1985, 2-3]]

Digital Library

Google Scholar

[7]

Blair, D. and Maron, M. An Evaluation of Retrieval Effectiveness for a Full-Text Document-Retrieval System. Commun. ACM 28, 3 (1985), 289-299.]]

Digital Library

Google Scholar

[8]

Boguraev, B. and Briscoe, T. Large Lexicons for Natural Language Processing: Utilising the Grammar Coding System ofLDOCE. Comput. Linguist. i3 (1987), 3-4.]]

Digital Library

Google Scholar

[9]

Brcsnan, J. Ed. The Mental Representation of Grammatical Relations. MIT Press, Cambridge, Mass., 1982.]]

Google Scholar

[10]

Byrd, R., Calzolari, N., Chodorow, M., Klavans, J., Neff M. and Rizk, O. Tools and Methods for Computational Lexicology. Comput. Linguist. 13 (1987), 3-4.]]

Digital Library

Google Scholar

[11]

Calder, J., Reape, M. and Zeevat, H. An Algorithm for Generation in Unification Categorial Grammar. In Proceedings of the 4th Conference of the European Chapter of the ACL, (1989), pp. 233-240.]]

Digital Library

Google Scholar

[12]

Carlson, G. Generic Terms and Generic Sentences. dr. of Philos. Logic 11, 2 (1982), 145-181.]]

Google Scholar

[13]

Cohen, P. and Kjeldsen, R. Information Retrieval by Constrained Spreading Activation in Semantic Networks. Inf Proces. Manage. 23 (1987), 225-268.]]

Digital Library

Google Scholar

[14]

Cohen, R.M., McCandless, T.P. and Rich, E.A. A Problem Solving Approach to Human- Computer Interface Management. In Proceedings of Workshop on Blackboard Systems, IJCAI-89, (1987).]]

Google Scholar

[15]

Dahl, D., Palmer, M. and Passonneau, R. Nominalizations in Pundit. In Proceedings of the 25th Annual Meeting of the ACL, (1987).]]

Digital Library

Google Scholar

[16]

Dorr, BonnieJ. UNITRAN: An Interlingual Approach to Machine Translation. In Proceedings of the Sixth National Conference of AAAI, (1987).]]

Google Scholar

[17]

Earley, J. An Efficient Context-Free Parsing Algorithm. Commun. ACM 13 (1970), 94-102.]]

Digital Library

Google Scholar

[18]

Englemore, R. and Morgan, T. Ed. Blackboard Systems. Addison-Wesley, Reading, Mass., 1988.]]

Google Scholar

[19]

Finin, T. The Semantic Interpretation of Compound Nominals. Ph.D. Univ. of Illinois, 1980.]]

Digital Library

Google Scholar

[20]

Fox, E.A. Development of the CODER System: a Testbed for Artificial Intelligence Methods in Information Retrieval. Inf. Process. Manage. 23, 4 (1987), 341-366.]]

Digital Library

Google Scholar

[21]

Fox, E.A. Improved Retrieval Using a Relational Thesaurus for Automatic Expansion of Boolean Logic Queries. In RelationaIModels of the Lexicon, M. Evens, Ed., Cambridge University Press, 1988, pp. 199-210.]]

Crossref

Google Scholar

[22]

Furnas, G., Deerwester, S., Dumais, S., Landauer, T. Harshman, R., Streeter, L. and Lochbaum, K. Information Retrieval using a Singular Value Decomposition Model of Latent Semantic Structure. In Proceedings of SIGIR-88, (1988), pp. 465-480.]]

Digital Library

Google Scholar

[23]

Grice, H.P. Logic and Conversation. In Studies in Syntax, Vol. (III), P. Cole and J.L. Morgan, Ed., Seminar Press, New York, 1975.]]

Google Scholar

[24]

Grosz, B.J., Appelt, D.E., Martin, P.A. and Pereira, EC.N. TEAM: An Experiment in the Design of Transportable Natural-Language Interfaces. Artff Intell 32, 2, (May 1987), 173-2~,3.]]

Digital Library

Google Scholar

[25]

Heim, I. The Semantics of Definite and Indefinite Noun Phrases. Ph.D. thesis, Univ. of Massachusetts, 1982.]]

Google Scholar

[26]

Hendrix, G.G., Sacerdoti, E.D., Sagalowicz, D., and Slocum, J. Developing a Natural Language Interface to Complex Data. ACM 7?ans. Database Syst. 3 (1978), 105-147.]]

Digital Library

Google Scholar

[27]

Hirschberg, J. Toward a Redefinition of Yes/No Questions. In Proceedings of the lOth International Conference on Computational Linguistics (COLING) (1984).]]

Digital Library

Google Scholar

[28]

Hirst, G. Semantic Interpretation and the Resolution of Ambiguity. Cambridge University Press, 1987.]]

Digital Library

Google Scholar

[29]

Hobbs,J. Overview of the TACITUS Prqiect. Comput. Linguist. 12, 3 (1986).]]

Digital Library

Google Scholar

[30]

Hobbs, J. and Stickel, M. Interpretation as Abduction. In Proceedings of the 26th Annual Meeting of the Association for Computational Linguistics, (1988), pp. 95-103.]]

Digital Library

Google Scholar

[31]

Hollan,J.D., Miller, J., Rich, E. and Wilner, W. Knowledge Bases and Tools for Building Integrated Multimedia Intelligent Interfaces. In Proceedings of the 14'brkshop on Architectures for Intelligent Interfaces: Elements and Prototypes, (1988).]]

Digital Library

Google Scholar

[32]

Hovy, E.H. Generating Language with a Phrasal Lexicon. In Natural Language Generation Systems, D.D. McDonald & L. Bolc, Eds., Springer-Verlag, New York, 1988.]]

Digital Library

Google Scholar

[33]

IBM Japan. The Art of Machine Translation: English to Japanese. IBM Res. Mag. 27, 4 (1989).]]

Google Scholar

[34]

Jacobs, P.S. Achieving Bidirectionality. In Proceedings of the 12th International Conference on Comput. Linguist. (COLING) (1988), pp. 267-269.]]

Digital Library

Google Scholar

[35]

Kamp, H. and Rohrer, C. Tense in Texts. In Meaning; Use and the Interpretation of Language, Bauerle, Schwartz and yon Stechow, Eds., de Gruyter, Berlin, 1983.]]

Google Scholar

[36]

Kamp, H. A Theory of Truth and Semantic Representation. In 7~uth, Interpretation, and Information, Groenendijk, J., Janssen, T. and Stokhof, M., Eds., Foris, Dordrecht, Germany, 1984.]]

Google Scholar

[37]

Kaplan, R. A General Syntactic Processor. In Natural Language Processing. R. Rustin, Ed., New York: Algorithmics Press, 1973.]]

Google Scholar

[38]

Kolodner, J.L. Retrieval and Organizational Strategies in Conceptual Memog,: A Computer Model. Lawrence Erlbaum Associates, Hillsdale, N.J., 1984.]]

Google Scholar

[39]

Lenat, D.B. and Guha, R.V. Building Large Knowledge Based Systems. Addison Wesley, Reading, Mass., 1990.]]

Digital Library

Google Scholar

[40]

Lenat, D.B., Guha, R.V., Pittman, K., Pratt, D., and Shepherd, M. Cyc: Toward Programs with Common Sense. Commun. ACM (August 1990).]]

Digital Library

Google Scholar

[41]

Mann, W.C. and Matthiessen, C. Nigel: A Systemic Grammar For Text Generation. USC Information Sciences Institute Tech. Rep. RR-83-105, 1983.]]

Google Scholar

[42]

McDonald, D.D. and Meteer, M.W. From Water to Wine: Generating Natural Language Text from Today's Application Programs. In Proceedings of the Second ACL Conference on Applied Natural Language Processing, (1988).]]

Digital Library

Google Scholar

[43]

McKeown, K.R. Generating Natural Language 7~xt in Response to Questions About Database Structure. Ph.D. Thesis Univ. of Pennsylvania, 1982.]]

Digital Library

Google Scholar

[44]

Nirenburg, S. Knowledge-Based Machine Translation. Mach. 7?ans. 4, 1 (1989), 5-20.]]

Google Scholar

[45]

Oehrle, R., Bach, E. and Wheeler, D. (Ed.) Categorical Grammars and Natural Language Structures. Reidel, Dordrecht, Holland, 1987.]]

Google Scholar

[46]

Pereira, E and Warren, D. Parsing as Deduction. In Proceedings of the 21st Annual Meeting of the Association for Computational Linguistics, (1983), pp. 95-103.]]

Digital Library

Google Scholar

[47]

Rich, E.A. The Gradual Expansion of Artificial Intelligence. IEEE Comput. (May 1984), 4-12.]]

Digital Library

Google Scholar

[48]

Rich, E.A. and Luper-Foy, S. An Architecture for Anaphora Resolution. In Proceedings of the Second ACL Conference on Applied Natural Language Processing; (1988).]]

Digital Library

Google Scholar

[49]

Sager, N., Friedman, C. and Lyman, M.S. Medical Language Processing. Addison-Wesley, Reading, Mass., 1987.]]

Google Scholar

[50]

Salton, G., Fox, E.A. and Wu, H. Extended Boolean Information Retrieval. Commun. ACM 26, 12 (1983), 1022-0136.]]

Digital Library

Google Scholar

[51]

Schank, R.C. and Riesbeck, C.K. Ed. Inside Computer Understanding: Five Programs Plus Miniatures. Erlbaum, Hillsdale, N.J., 1981.]]

Digital Library

Google Scholar

[52]

Scott, B.E. The Logos System. MT SUMMIT II Conference--Munich, 1989.]]

Google Scholar

[53]

Shieber, S.M. A Uniform Architecture for Parsing and Generation. In Proceedings of the 12th International Conference on Computational Linguistics (COLING), (1988), pp. 614-619.]]

Digital Library

Google Scholar

[54]

Shieber, S.M., van Noord, G., Moore, R.C. and Pereira, EC.N. A Semantic-Head-Driven Generation Algorithm for Unification-Based Formalisms. In Proceed,'ngs of the 27th Annual Meeting of the Association for Computational Linguistics, (1989).]]

Digital Library

Google Scholar

[55]

Simmons, R.F. Semantic Networks: Their Computation and Use for Understanding English Sentences. In Computer Models of Thought and Language, R.C. Schank and K.M. Colby, Eds., Freeman, San Francisco, 1973.]]

Google Scholar

[56]

Spark-Jones, K. and Tait, J.I. Automatic Search Term Variant Generation. J. Docu. 40, 1 (1984), 50-66.]]

Google Scholar

[57]

Stallard, D. The Logical Analysis of Lexical Ambiguity. In Proceedings of the 25th Annual Meeting of the Association for Computational Linguistics, (1987).]]

Digital Library

Google Scholar

[58]

Thomason, R. Ed. Formal Philosophy: Selected Papers of Richard Montague. Yale University Press, 1974.]]

Google Scholar

[59]

Webber, B.L. (Ed.) Compu. Linguis.: Speciallssue on 2~nse and Aspect, 1988.]]

Google Scholar

[60]

Winograd, T. and Flores, F. Understanding Computers and Cognition.' A New Foundation for Design. Ablex, Norwood, N.J., 1986.]]

Digital Library

Google Scholar

[61]

Wittenburg, K. A Parser for Portable NL Interfaces Using Graph-Unification-Based Grammars. In Proceedings of the Fifth National Conference of AAAI 86, (1986).]]

Google Scholar

[62]

Woods, W.A. Transition Network Grammars for Natural Language Analysis. Comm of ACM 13 (1970), 591-606.]]

Digital Library

Google Scholar

[63]

Woods, W.A. Cascaded ATN Grammars. Am. J. Compu. Linguis. 6, 1 (1980), 1-12.]]

Digital Library

Google Scholar

[64]

Wroblewski, D.A. and Rich, E.A. Luke: An Experiment in the Early Integration of Natural Language Processing. In Proceedings of the Second ACL Conference on Applied Natural Language-Processing. (1988).]]

Digital Library

Google Scholar

Cited By

View all

Chi WTang TSalleh NMukred MAlSalman HZohaib M(2024)Data Augmentation With Semantic Enrichment for Deep Learning Invoice Text ClassificationIEEE Access10.1109/ACCESS.2024.338786012(57326-57344)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3387860
Rahgozar AInkpen D(2016)Poetry Chronological ClassificationProceedings of the 29th Canadian Conference on Artificial Intelligence on Advances in Artificial Intelligence - Volume 967310.1007/978-3-319-34111-8_18(131-136)Online publication date: 31-May-2016
https://dl.acm.org/doi/10.1007/978-3-319-34111-8_18
Sharma AKushwaha D(2011)Natural language based component extraction from requirement engineering document and its complexity analysisACM SIGSOFT Software Engineering Notes10.1145/1921532.192154736:1(1-14)Online publication date: 24-Jan-2011
https://dl.acm.org/doi/10.1145/1921532.1921547
Show More Cited By

Index Terms

Knowledge and natural language processing
1. Computing methodologies
  1. Artificial intelligence
    1. Knowledge representation and reasoning
    2. Natural language processing
      1. Natural language generation

Recommendations

The restriction language for computer grammars of natural language

Over the past few years, a number of systems for the computer analysis of natural language sentences have been based on augmented context-free grammars: a context-free grammar which defines a set of parse trees for a sentence, plus a group of ...
Transition network grammars for natural language analysis

The use of augmented transition network grammars for the analysis of natural language sentences is described. Structure-building actions associated with the arcs of the grammar network allow for the reordering, restructuring, and copying of constituents ...
Applying semantic-based probabilistic context-free grammar to medical language processing - A preliminary study on parsing medication sentences

Semantic-based sublanguage grammars have been shown to be an efficient method for medical language processing. However, given the complexity of the medical domain, parsers using such grammars inevitably encounter ambiguous sentences, which could be ...

Reviews

Reviewer: Graeme J. Hirst

Researchers in computational linguistics have long acknowledged that understanding language requires understanding the world. It is people's extensive commonsense knowledge of the world that enables them to perform with ease so many of the linguistic tasks that are difficult for computers: resolving ambiguity, finding referents for pronouns, determining a speaker's true intent—in general, determining which of many possibilities makes the most sense. Most natural language systems have worked only in small, toy domains, because of the daunting task of providing the massive knowledge bases needed for larger domains. The Cyc project to encode large bodies of knowledge for use in AI systems [1] now offers the opportunity to build a large-scale natural language system and see if methods developed for small systems do indeed scale up, to see whether a large knowledge base does make all the difference. The Knowledge-Based Natural Language (KNBL) project is the natural language companion to the Cyc project. It has two main components: Lucy converts an English sentence to a representation in CycL, the language of the Cyc knowledge base, and Koko produces English from CycL expressions. These systems combine a number of state-of-the-art techniques in language understanding and add a few new techniques. For example, the idea of a chart, a two-dimensional structure used in parsing, is generalized to a three-dimensional structure in which the third dimension represents alternatives used in semantic processing. The linguistic knowledge in Lucy and Koko is carefully separated from the world knowledge in the Cyc knowledge base. The interesting question, of course, is what Lucy gains from having Cyc available as a resource. While the authors necessarily gloss over details in this overview paper, it seems that Lucy is able to get from Cyc the information necessary to solve such puzzles as lexical ambiguity, nominal compounds, and metonymy. For example, it can determine that “corn oil” is made from corn, because Cyc knows that corn is the sort of stuff that can be processed into oil. On the other hand, “baby oil” is oil for the use of babies. This kind of reasoning is not new, but KNBL may be the first system in which the knowledge base that supports it is not built specifically for this purpose. In addition to Lucy and Koko, the authors describe a tool, Luke, that helps a system builder add words and their senses to the system dictionary. Luke is designed so that its user need not be a natural language expert; Luke uses the meanings of words to guess many of their syntactic properties and presents these guesses for confirmation. Because it can be difficult to navigate in a large lexical structure, Luke may be instructed in English. Much work remains to be done before it can be said whether the use of a massive knowledge base will do what we hope it will for natural language systems. The KNBL project is reason for optimism.

Access critical reviews of Computing literature here

Become a reviewer for Computing Reviews.

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

Communications of the ACM Volume 33, Issue 8

Aug. 1990

129 pages

ISSN:0001-0782

EISSN:1557-7317

DOI:10.1145/79173

Editor:
Peter J. Denning
NASA Ames Research Center, Moffett Field, CA

Issue’s Table of Contents

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 01 August 1990

Published in CACM Volume 33, Issue 8

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tag

parsing

Qualifiers

Article

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

34
Total Citations
View Citations
2,528
Total Downloads

Downloads (Last 12 months)229
Downloads (Last 6 weeks)37

Reflects downloads up to 27 Jan 2025

Other Metrics

View Author Metrics

Citations

Cited By

View all

Chi WTang TSalleh NMukred MAlSalman HZohaib M(2024)Data Augmentation With Semantic Enrichment for Deep Learning Invoice Text ClassificationIEEE Access10.1109/ACCESS.2024.338786012(57326-57344)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3387860
Rahgozar AInkpen D(2016)Poetry Chronological ClassificationProceedings of the 29th Canadian Conference on Artificial Intelligence on Advances in Artificial Intelligence - Volume 967310.1007/978-3-319-34111-8_18(131-136)Online publication date: 31-May-2016
https://dl.acm.org/doi/10.1007/978-3-319-34111-8_18
Sharma AKushwaha D(2011)Natural language based component extraction from requirement engineering document and its complexity analysisACM SIGSOFT Software Engineering Notes10.1145/1921532.192154736:1(1-14)Online publication date: 24-Jan-2011
https://dl.acm.org/doi/10.1145/1921532.1921547
Tao XMiao Y(2008)Interest Based Learning Activity NegotiationProceedings of the 2008 International Conference on Cyberworlds10.1109/CW.2008.104(58-64)Online publication date: 22-Sep-2008
https://dl.acm.org/doi/10.1109/CW.2008.104
Hudaib AHammo BAlkhader Y(2007)Towards software requirements extraction using natural language approachProceedings of the 6th WSEAS International Conference on Software Engineering, Parallel and Distributed Systems10.5555/1353801.1353829(155-160)Online publication date: 16-Feb-2007
https://dl.acm.org/doi/10.5555/1353801.1353829
Veale TKeane M(2007)CONCEPTUAL SCAFFOLDING: A SPATIALLY FOUNDED MEANING REPRESENTATION FOR METAPHOR COMPREHENSIONComputational Intelligence10.1111/j.1467-8640.1992.tb00377.x8:3(494-519)Online publication date: 2-Apr-2007
https://doi.org/10.1111/j.1467-8640.1992.tb00377.x
Schumaker RGinsburg MChen HLiu Y(2007)An evaluation of the chat and knowledge delivery components of a low-level dialog systemDecision Support Systems10.1016/j.dss.2006.07.00142:4(2236-2246)Online publication date: 1-Jan-2007
https://dl.acm.org/doi/10.1016/j.dss.2006.07.001
Alkhader YHudaib AHammo B(2006)Experimenting With Extracting Software Requirements Using NLP Approach2006 International Conference on Information and Automation10.1109/ICINFA.2006.374136(349-354)Online publication date: Dec-2006
https://doi.org/10.1109/ICINFA.2006.374136
Luck KPirlein T(2005)Constructing a context for LEU/2Text Understanding in LILOG10.1007/3-540-54594-8_88(701-710)Online publication date: 4-Jun-2005
https://doi.org/10.1007/3-540-54594-8_88
Emde W(2005)Managing lexical knowledge in LEU/2Text Understanding in LILOG10.1007/3-540-54594-8_60(167-179)Online publication date: 4-Jun-2005
https://doi.org/10.1007/3-540-54594-8_60
Show More Cited By

View Options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Abstract

References

Cited By

Index Terms

Recommendations

The restriction language for computer grammars of natural language

Transition network grammars for natural language analysis

Applying semantic-based probabilistic context-free grammar to medical language processing - A preliminary study on parsing medication sentences

Reviews

Access critical reviews of Computing literature here

Comments

Information

Published In

Publisher

Publication History

Permissions

Check for updates

Author Tag

Qualifiers

Contributors

Other Metrics

Bibliometrics

Article Metrics

Other Metrics

Citations

Cited By

View options

PDF

eReader

Login options

Full Access

Share

Share this Publication link

Share on social media

Affiliations