Online Store

Geometry.Net - the online learning center
Home  - Mathematical_Logic - Logic Of Natural Languages
Page 1     1-58 of 58    1 

1. Presentations -> Business Natural Languages Development In Ruby
Business natural languages use natural language to represent business Logic. Business natural languages are expressed as descriptive and maintainable Natural Languages Devel
Presentation: "Business Natural Languages Development in Ruby"
Track The Rise of Ruby Time : Friday 09:30 - 10:30 Location : Franciscan II Abstract Since the introduction of computers to the general workforce businesses have searched for a solution that will enable subject matter experts to specify the business logic of an application. This solution is highly sought after since it will allow the application to be changed without the assistance of a programmer. Programmers are still required to create the application; however, the application is written in a way that empowers the subject matter experts to maintain the business logic. Enabling the subject matter expert greatly increases efficiency of maintaining an application as the needs of the business change. Using a Domain Specific Language (DSL) is the most recent solution to this problem. A Business Natural Language is a Domain Specific Language; however, not all Domain Specific Languages are Business Natural Languages. Business Natural Languages use natural language to represent business logic. Business Natural Languages are expressed as descriptive and maintainable phrases. For example, a marketing executive for an airline could specify point award descriptions as:

2. Business Natural Languages - Limitations
Business Logic can be highly complex and unrelated. A Business natural Language becomes more complicated as it expands the scope of problems it addresses.
[ Blog ] Jay Fields
Blog: [ View ] [ RSS ] Work: [ ThoughtWorks ]
Business Natural Languages - Domain Specific Languages for empowering subject matter experts
Limitations of Business Natural Languages
Business logic can be highly complex and unrelated. A Business Natural Language becomes more complicated as it expands the scope of problems it addresses. As the complication increases the value of the language decreases. Therefore, when designing a Business Natural Language it should address only related business logic. One application can contain several Business Natural Languages. By limiting the scope of each Business Natural Language it will be easier to understand and use.
Every application should not include a Business Natural Language. In fact, businesses should not invest in creating a Business Natural Language unless the application contains logic specific to the business. An example of an application that, despite being a complex application, likely doesn't warrant the use of a Business Natural Language is a content management system (CMS). Creating a CMS is no easy task; however, the business simply wants an application that allows them to easily display static information. There is no reason to design a Business Natural Language when an application, such as a CMS, does not contain any business logic. Business Natural Languages are designed to empower people who contain specific knowledge. By empowering these individuals the business can run more efficiently. However, Business Natural Languages should not be used as an excuse to delegate work. For example, even though you could empower a subject matter expert to specify the database connection string, you shouldn't do it. Maintaining a database connection string is the responsibility of the IT staff. Creating a Business Natural Language is unnecessary in this scenario since the IT staff should be perfectly comfortable with XML, YAML, or some other form of configuration file.

3. Of Thought & Action: Of Inductive Logic, Perceptions, And Natural Languages
Of inductive Logic, perceptions, and natural languages He reads this and hates all men. Looking forward to your series on Logic and fallacies.
Public, or something like it. Probably not a diary though.
Tuesday, September 18, 2007
Of inductive logic, perceptions, and natural languages
He reads this and hates all men. Frivolous as it seems, I am tempted to ask, why not just Pakistani men?
The problem is, of course, central to all of inductive reasoning . How strong is the inductive link in your generalization? The solutions are very context-specific. And the interpretation of context is very experience-specific. One of the main irritants with life is that statistical inference is not readily obtainable. Hence, one's experience becomes one's truth. And we all have different versions of the truth - each one, a priori, as true as the others. Post-modernism suddenly seems attractive.
Given the sentence "Rand is popular in the girls' hostels", how do you interpret it? Does the speaker want to suggest that Rand is more popular than unpopular in the girls' hostels? Or does he want to say that Rand is more popular in the girls' hostels than in the boys' hostels.
Mathematically, let's define a threshold of popularity, say x% readership, and denote the reader base of Rand (expressed as a percentage) among hostelite girls as P(h-girls), among hostelite boys as P(h-boys), and among non-hostelite girls as P(nh-girls). What does the speaker want to say by "Rand is popular in the girls' hostels" ?

4. First-Order Predicate Logic
The two important features of natural languages whose Logic is captured in the predicate calculus are the terms every and some and their synonyms,
First-Order Predicate Logic
predicates in natural languages
quantifiers in natural languages

predicate logics
see also:
and formal descriptions of a first-order predicate logic.
semi-formal and formal descriptions of propositional logic.
Predicates in Natural Languages
A predicate is a feature of language which you can use to make a statement about something, e.g. to attribute a property to that thing. If you say "Peter is tall", then you have applied to Peter the predicate "is tall". We also might say that you have predicated tallness of Peter or attributed tallness to Peter. A predicate may be thought of as a kind of function which applies to individuals (which would not usually themselves be propositions) and yields a proposition. They are therefore sometimes known as propositional function s Analysing the predicate structure of sentences permits us to make use of the internal structure of atomic sentences, and to understand the structure of arguments which cannot be accounted for by propositional logic alone.

5. Sau La Lodtua: 93/2
The excellent implementation of firstorder Logic is not a major improvement over natural language; natural languages (at least some of them) have had
A page from the Loglan web site (From Lognet 93/2 . Used with the permission of The Loglan Institute, Inc.
Sau La Lodtua
(From the Logic-Worker = Logician)
by M. Randall Holmes My apologies for not providing a column in the last issue; contrary to popular belief, professors are busy people! I'm still not ready with anything very concrete this time, so I thought I'd give some brief remarks on my opinion of the significance of Loglan and its present condition from my position as a mathematical logician (and amateur philosopher, you will find). These are largely inspired by a recent dialogue I had with Alan Gaynor. Alan Gaynor's thesis is that Loglan is a new and revolutionary "implement of reason." I won't go into the details of his argument; but this claim forced me to think out my own position on this subject yet again. I disagree with Gaynor in the following sense: I do not think that Loglan is capable of expressing essentially more than a natural language like English. This means, obviously, that I expect negative results from tests of a strong version of the Sapir-Whorf hypothesis using Loglan! While Loglan might facilitate certain kinds of thinking to some degree, I do not believe that it implements any basically new kind of thinking, or even improves existing kinds of thinking to the point where qualitative improvement in thought can be expected. One of my sources for evidence for this claim is Quine's book Word and Object, in which he gives an analysis of the logical structure of natural language (using English, of course) which looks like an engineering blueprint for Loglan! This seems to indicate (as I will claim explicitly below) that, far from being basically different from existing natural languages (at least some of them), Loglan is a refinement of natural language.

6. News And Events: Associate Professorship (Forsteamanuensis) In Logic And Natural
The position is within the research areas of Logic and Computational Linguistics and belongs to the research group called Logic and natural languages (LNS).

7. Logic And Natural Languages (LNS), Dept. Of Informatics, UiO
Ifi research Logic and natural languages We have the main responsibility for the Logic teaching within the department and within the faculty where we
UiO - web pages UiO - persons WWW - Google About The University Academics Student Life University Library ... Ifi research: Logic and natural languages
Logic and natural languages
Home Teaching Available Master Projects Persons ... Research
Programs: Courses:

Editors: LNS
Document created: 22.03.2007 Get in touch with the University of Oslo

8. IngentaConnect Negation In Logic And In Natural Language
The resulting Logic (extended independencefriendly Logic) explains several regularities in natural languages, e.g., why contradictory negation is a barrier
var tcdacmd="dt";

9. Linguistics 575A: Quantification, Semantic Representations, And The Syntax-Seman
Faltz, Leonard M. 1995. Towards a Typology of natural Logic. In Bach, E., E.Jelinek, A. Kratzer, and B.H. Partee (eds) Quantification in natural languages.
Linguistics 575A: Quantification, Semantic Representations and the Syntax-Semantics Interface
Course Info
Instructor Info
Semantic representations, as used in computational linguistics, typically require that all nominal indices be bound by an appropriate quantifier. In some languages (including English), there is a syntactic position which corresponds nicely (specifier of NP, filled by determiners). Crosslinguistically, however, we find languages that rarely, or never, express quantification within the NP. In the strictly compositional framework assumed in the Grammar Matrix (and other implemented work on HPSG), this leads to a mismatch between syntax and semantics which is handled by unary rules introducing quantifiers. In this seminar, we will explore these issues in depth, seeking to discover whether current standard semantic representations are appropriate crosslinguistically, what alternatives may exist, and the theoretical origins of the current state of affairs. Note: To request academic accommodations due to a disability, please contact Disabled Student Services, 448 Schmitz, 206-543-8924 (V/TTY). If you have a letter from Disabled Student Services indicating that you have a disability which requires academic accommodations, please present the letter to the instructor so we can discuss the accommodations you might need in this class.

10. Representing Knowledge Soup
The framework of knowledge soup can span the gap between natural language semantics and formal systems of Logic. natural languages must do everything
Representing Knowledge Soup
In Language and Logic
John F. Sowa Chief Scientist VivoMind LLC
A talk presented at the Conference on Knowledge and Logic
Knowledge Soup
  • The contents of the human mind are inconsistent, loosely organized, and in perpetual flux.
  • The mind is not a highly organized knowledge base.
  • And it is not a large fuzzy peach.
  • A better term is knowledge soup : Fluid, lumpy, with chunks that float in and out of awareness.
Aspects of the Soup
  • Overgeneralizations: Birds fly. But what about penguins? A day-old chick? A bird with a broken wing? A stuffed bird? A sleeping bird? A bird in a cage?
  • Abnormal conditions: If you have a car, you can drive from New York to Boston. But what if the battery is dead? Your license has expired? There is a major snowstorm?
  • Incomplete definitions: An oil well is a hole drilled in the ground that produces oil. But what about a dry hole? A hole that has been capped? A hole that used to produce oil? Are three holes linked to a single pipe one oil well or three?
  • Conflicting defaults: Quakers are pacifists, and Republicans are not.

11. The Language Of Science / Logic (Jody Azzouni)
But there is another important role for threefoldcharacterized Logic. This is in the regimentation of natural languages. One reason that it took so long to

12. Introduction To Natural Language Semantics
Logical languages are then developed as formal metalanguages to natural language. Subsequent chapters address propositional Logic, the syntax and semantics
Introduction to Natural Language Semantics This introduction is concerned with the semantics of natural languages. Semantics is defined as the study of meaning expressed by elements of a language or combinations thereof. These combinations of languagewhether written or spokenare used to convey information, and are linked with kinds of events, with states of mind, etc. Speaker and hearer use language to communicate. The text examines what issues semantics, as a theory of meaning, should address; determining what the meanings of words of the language are and how to semantically combine elements of a language to build up complex meanings. Logical languages are then developed as formal metalanguages to natural language. Subsequent chapters address propositional logic, the syntax and semantics of (first-order) predicate logic as an extension of propositional logic, and Generalized Quantifier theory. Going beyond extensional theory, de Swart relativizes the interpretation of expressions to times to account for verbal tense, time adverbials and temporal connectives and introduces possible worlds to model intensions, modal adverbs and modal auxiliaries. This broad overview of natural language semantics should cover most of the points addressed in an introductory course. Numerous exercises punctuate each chapter and an example exam based on the materials presented is included, making this volume a perfect textbook and resource for any undergraduate or graduate-level introductory course in semantics.

13. OUP: UK General Catalogue
It presents a broad view of the semantics and Logic of quantifier expressions in natural languages and, to a slightly lesser extent, in Logical languages.

14. MasterÕs Program In Computational Linguistics
The language of firstorder Logic, and introduction to truth-theoretic semantics for formal and natural languages. Compositionality.
MasterÕs Program in Computational Linguistics
USC offers two programs in the area of Computational Linguistics (also called Natural Language Processing and Human Language Technology This program offers an MS degree. It is centered in the Department of Linguistics and focuses on issues in Linguistics. Its faculty are primarily experts in Linguistics. The other program ( click here ) offers MS and PhD degrees in Computer Science with an emphasis on Human Language Technology / Computational Linguistics, and focuses on all aspects of computational linguistics. Its faculty are primarily experts in Computer Science, and are members of the world-renowned Natural Language research groups at the Information Sciences Institute (ISI) and Institute for Creative Technology (ICT).
Joseph Aoun Professor , Linguistics, USC Michael Arbib Professor , Computer Science, USC Robert Belvin Computer Scientist , HRL Laboratories; Lecturer , Linguistics, USC Bonnie Glover Stalls Adjunct Professor , Linguistics, USC Shrikanth Narayanan Assistant Professor , Electrical Engineering, USC Jean-Roger Vergnaud Professor , Linguistics, USC Research Areas The faculty members comprise a group of internationally renowned scholars from Linguistics . Their areas of research help to shape research in the program. Within the field of Linguistics, prevailing models of formal grammar have been shaped to a great extent by these scholars whose expertise encompasses not only grammatical theory but a wide range of languages and language families.

15. Gyula Klima: Approaching Natural Language Via Mediaeval Logic
In fact, I suspect that the following view of the relation between Logic and quantificational and referential features of natural language is fairly widely
Gyula Klima:
Approaching Natural Language via Mediaeval Logic
(Appeared in: J. Bernard-J. Kelemen: Zeichen, Denken, Praxis , Institut fur Sozio-Semiotische Studien: Vienna, pp. 249-267. To print the published version, click here
I think it is significant, and generally characteristic of the change of attitudes taken by logicians and philosophers of language in the last two decades towards the relationship between quantification theory and natural languages, that George Boolos , from whom this lengthy quotation derives, gives this detailed description of what may be called the paradigmatic view of this relationship only to raise several objections to it. This change of attitudes was mainly prompted by the recognition of a steadily growing body of anomalies in the application of quantification theory to natural languages. These anomalies may be gathered, roughly, under the following headings:
Mismatch of syntax
As is well-known, natural language sentences of evidently the same syntactic structure are represented by formulae of quantification theory of entirely different structure, while the same formula may have different "readings", expressible by natural language sentences of widely different syntax. Regarding these discrepancies, of course, one might say that there is no justifiable need of a strict correspondence between the syntactic structure of natural language sentences and the formulae representing them. After all, a logical semantics, which is to be a general semantics for all kinds of human languages, should precisely disregard accidental grammatical features of particular natural language expressions, and hence also the delusive grammatical structure of natural language sentences in general. All that is required for correspondence is that the formula should state correctly the truth conditions of the sentence which it represents, since it is only these truth conditions that determine the logical relations of sentences among each other.

16. CoLogNET - Logic For Natural Language Processing
FG is a series of conferences on Formal Grammar, held in conjunction with the European Summer School in Logic, Language and Information (ESSLLI),

CALL FOR PAPER Logic Engineering natural Language Semantics. Logic and Engineering of natural Language Semantics 2006 (LENLS2006)
Logic and Engineering of Natural Language Semantics 2006
a satelite international workshop of the JSAI 2006 annual conference

Location: Tokyo, Japan
Tower Hall Funabori

June 5-6, 2006
Chair: Eric McCready (Osaka University/Aoyama Gakuin University)
Invited Speaker:
Makoto Kanazawa (National Institute of Informatics)
Chung-Min Lee (Seoul National University)(tentative) Christopher Potts (U. MassachusettsAmherst) This workshop is co-sponsored by and the Japan Society for the Promotion of Science LENS is an annual international workshop focusing on formal semantics and is organized as a satellite of the Japanese Society for Artificial Intelligence conference. This year's workshop, the third LENS, will include a special session on formal pragmatics. In recent years there have been a number of exciting developments in this area. Researchers have applied game-theoretical and utility-theoretic techniques to problems such as Gricean communication and relevance

18. Natural Language - Wikipedia, The Free Encyclopedia
ter Meulen, Alice, 2001, Logic and natural Language, in Goble, Lou, ed., The Blackwell Guide to Philosophical Logic. Blackwell.
var wgNotice = ""; var wgNoticeLocal = ""; var wgNoticeLang = "en"; var wgNoticeProject = "wikipedia";
Natural language
From Wikipedia, the free encyclopedia
Jump to: navigation search In the philosophy of language , a natural language (or ordinary language ) is a language that is spoken, written , or signed by humans for general-purpose communication, as distinguished from formal languages (such as computer-programming languages or the "languages" used in the study of formal logic , especially mathematical logic ) and from constructed languages
edit Defining natural language
Though the exact definition is debatable, natural language is often contrasted with artificial or constructed languages such as Esperanto Latino Sine Flexione , and Occidental Linguists have an incomplete understanding of all aspects of the rules underlying natural languages, and these rules are therefore objects of study. The understanding of natural languages reveals much about not only how language works (in terms of syntax semantics phonetics phonology , etc), but also about how the human mind and the human brain process language. In linguistic terms, 'natural language' only applies to a language that has evolved naturally, and the study of natural language primarily involves native (first language) speakers.

19. Logic, Language And Computation Group
Now they are creating artificial agents and trying to provide them with effective Computational Logic and natural Language Processing… Local folklore
Group of Logic, Language and Computation [Main] [People] [Teaching] [Research] ... [Seminars] Local folklore
Mission Statement
From their first beginnings in ancient Greece and India, logic and the study of language have been closely entwined. Logic takes its source in the process of reasoning; language is the basic means for carrying it out it, as well as the principal vehicle for its communication. At times in their history, logic and the study of language have turned their faces away from each other in their quests for inspiration. The nineteenth century, for example, was a great period in the development of historical and comparative linguistics with a predominantly empirical emphasis; the early twentieth century was for logic a moment of triumph with its deep characterization of deductive reasoning as carried out in mathematics. But with the entry of the computer into all domains of life, logic and the study of language have taken up new challenges beyond their past achievements, and are looking again to each other for cooperation and synergy. The discipline of natural language processing has been born, with its objectives of computational analysis, interpretation and generation of text, speech and dialogue. The very process of computation provides a reference against which to compare the hidden operations of the human mind. Logic has provided some of the essential ingredients for constructing artificial programming languages, and seeks to unlock the keys to forms of human reasoning beyond deduction, opening onto the exciting domain coming to be known as the practical logic of cognitive systems.

Charles Grant Brown, Gregers Koch (Eds.) natural Language Understanding and Logic Programming, III, Proceedings of the Third International Workshop on
Natural Language Understanding and Logic Programming Workshop
3. Natural Language Understanding and Logic Programming Workshop 1991: Stockholm, Sweden
Charles Grant Brown Gregers Koch (Eds.): Natural Language Understanding and Logic Programming, III, Proceedings of the Third International Workshop on Natural Language Understanding and Logic Programming, Stockholm, Sweden, 23-25 January, 1991. North-Holland, 1991, ISBN 0-444-89149-8
2. Natural Language Understanding and Logic Programming Workshop 1987: Vancouver, Canada
Patrick Saint-Dizier (Eds.): Natural Language Understanding and Logic Programming, II, Proceedings of the Second International Workshop on Natural Language Understanding and Logic Programming, Vancouver, Canada, 17-19 August, 1987. North-Holland, 1988, ISBN 0-444-70408-6
1. Natural Language Understanding and Logic Programming Workshop 1984: Rennes, France
Patrick Saint-Dizier (Eds.): Natural Language Understanding and Logic Programming, Proceedings of the First International Workshop on Natural Language Understanding and Logic Programming, Rennes, France, 18-20 September, 1984. North-Holland, 1985, ISBN 0-444-87714-2
DBLP: [ Home Author Title Conferences ... Journals
Sat Dec 22 21:44:53 2007 by Michael Ley

We shall uncover a natural alliance between natural language and Logic programming, which was apparent in the beginnings of the latter and is becoming again
Logic Programming and Natural Language
Veronica Dahl
Tutorial Abstract
Some Sample References
  • Alshawi, H. (1992) The Core Language Engine, MIT Press, 1992. Chomsky, N. (1982) Lectures on Government and Binding, the Pisa Lectures, 2nd (revised) Edition", Foris Publications, Holland Colmerauer, A. (1978) Metamorphosis Grammars. Lecture Notes in Computer Science, Springer-Verlag, 63, pp. 133-189. Carpenter, B. (1992) The Logic of Typed Feature Structures- with applicati ons to unification grammars, logic programs and constraint resolution, Cambridge University Press, 1992. Veronica Dahl (1994) Natural Language Processing and Logic Programming. In: Journal of Logic Programming, vols. 19,20, pp. 681-714, 1994. Veronica Dahl, Paul Tarau and Renwei Li (1997). Assumption Grammars for Natural Language Processing. To appear in: Proc. 1997 International Conference on Logic Programming, Belgium, July 1997. Stephen Rochefort, Veronica Dahl, and Paul Tarau (1997) Controlling Virtual Worlds through Extensible Natural Language. In AAAI Sym- posium on NLP for the WWW, Stanford University, CA, 1997. Zaiane, O., Fall, A., Rochefort, S., Dahl, V. and Tarau, P. (1997) Concept-Based Retrieval using Controlled Natural Language. Submitted to NLDB'97.

22. NLULP-02: Natural Language Understanding And Logic Programming
NLULP02 natural Language Understanding and Logig Programming.
The 7th International Workshop on
Natural Language Understanding and Logic Programming
An affiliated workshop with ICLP , as part of FLoC'02
Copenhagen, Denmark, 28 July, 2002
The International Workshop on Natural Language Understanding and Logic Programming was first organized in Rennes, France, in 1984 . Since then similar workshops took place in Vancouver, Canada (1987) Dalgharten, Sweden (1991) Nara, Japan (1993) Lisbon, Portugal (1995) and most recently, the 6th NLULP took place in Las Cruces, New Mexico in December 1999 , as part of the International Conference on Logic Programming (ICLP'99). This year, NLULP is affiliated again with ICLP, The International Conference on Logic Programming , which is held as part of FLoC'02, The 2002 Federated Logic Conference , the major computational logic event of the year. The Workshop aims to cover all aspects of the intersection of Natural Language Understanding with Logic Programming and Constraint (Logic) Programming, both theoretical and practical, in all levels of linguistic investigation. Special emphasis was given to works addressing the logical, mathematical and computational relationships between linguistic formalisms and logic programming.
Session 1: Opening session Shuly Wintner Welcome and Opening Keynote Speaker: Johan Bos Generating Speech Recognition Grammars with Compositional Semantics from Unification Grammars Coffee Break Session 2: Formalisms Mike Daniels and Detmar Meurers Improving the Efficiency of Parsing with Discontinuous Constituents

23. ScienceDirect - Language & Communication : Logic And Natural Language: On Plural
Logic and natural language On plural reference and its semantic and Logical significance, by Hanoch BenYami (Aldershot Ashgate, 2004)
Athens/Institution Login Not Registered? User Name: Password: Remember me on this computer Forgotten password? Home Browse My Settings ... Help Quick Search Title, abstract, keywords Author e.g. j s smith Journal/book title Volume Issue Page
Volume 27, Issue 1
, January 2007, Pages 28-40
Full Text + Links PDF (120 K) Related Articles in ScienceDirect Sense and secrecy
Language Sciences

Sense and secrecy
Language Sciences Volume 22, Issue 2 April 2000 Pages 193-202
Anthony Holiday
Full Text + Links PDF (100 K) Extensional vs. Intensional Logic ...
Philosophy of Logic

Extensional vs. Intensional Logic
Philosophy of Logic Pages 913-942 Jaroslav Peregrin Abstract From the Begriffsschrift to the philosophical investiga... From the Begriffsschrift to the philosophical investigations : Frege and Wittgenstein on the semantics of natural language Volume 17, Issue 1 January 1997 Pages 1-17 Janet Skupien Abstract Abstract + References PDF (1276 K) Frege's logic ... Handbook of the History of Logic Frege's logic Handbook of the History of Logic Volume 3 Pages 659-750 Peter M. Sullivan

24. The Role Of PROLOG (PROgramming And LOGic) In Natural Language Processing.
The field of Artificial Intelligence strives to produce computer programs that exhibit intelligent behavior. One of the areas of interest is the processing

25. The Non-Boolean Logic Of Natural Language Negation REYES Et Al
The NonBoolean Logic of natural Language Negation. MARIE LA PALME REYES*, JOHN MACNAMARA*, GONZALO E. REYES {dagger} and HOUMAN ZOLFAGHARI {dagger}

26. Template.1
Logic and natural Language The Logical tools found at this website can easily How much do we want Logic to imitate the structure of natural language? ahead/III.logic.language.html
Main Page Philosophical Terms Reconstructing an Argument Short List of Definitions ... Rotating Validity Exercises Glances Ahead: More to Think About III. Logic and Natural Language
The logical tools found at this website can easily convince us that the following
syllogism is a valid argument: 1. All men are mortal.
2. Socrates is a man.
3. Therefore, Socrates is mortal. We might then want to ask what form the argument takes, and see how we can represent it symbolically. We let the statement variable p stand for the first premise, the variable q stand for the second premise, and the variable r stand for the conclusion. None of these statements is a conjunction, implication, disjunction, or negation, so it seems as though our translation into statement variables is the simplest translation we can find. However, the translation tells us that our original valid argument is one of this form: 1. p.

27. PC AI - Natural Language Processing
From natural Language Processing to Logic for Expert Systems A Logic Based Approach to Artificial Intelligence, Thayse, A. (Ed) (1991) New York,
Where Intelligent Technology Meets the Real World Home Contents Search News ... Contact PC AI
Natural Language Processing
Overview Glossary Link Natural Language Processing SUBMIT YOUR SITE To Multimedia To Neural Networks
Natural Language Processing Information on the Internet
The MIT InfoLab Group The MIT InforLab Group develops intelligent interactive software systems that help people access information and solve problems on human terms. They conduct research in natural language processing and multimedia information access. The Natural Language Group at ISI Home page for the Natural Language Group at the Information Sciences Institute at the University of Southern California. Find information about the research performed by the group as well as several demonstrations. Natural Language Processing FAQ Find answers to frequently asked questions about natural language processing. Natural Language Processing Links Find links to natural language processing web sites. NLPLAB Web Home Page Learn about current research and find software and other resources on natural language processing.

28. Linguistic Agents Ltd.'s Streaming Logic: Smart Talker
To ease this interaction, privately held Linguistic Agents Ltd. in Jerusalem developed Streaming Logic, a technology that converts natural language into a

29. UT ML Group: Natural Language Learning
Our research in learning for natural language mainly involves applying statistical relational learning, inductive Logic programming, explanationbased
UT ML Group : Natural Language Learning
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 statistical relational learning inductive logic programming explanation-based learning , and other learning techniques for automatically constructing semantic parsers (e.g. database interfaces) and information extraction systems from training examples. However, we have also conducted research in learning for syntactic parsing, machine translation, language generation, word-sense disambiguation, morphology (past tense generation), and schema-based narrative understanding.
Demos of learning natural-language interfaces:
Also see

  • Learning for Semantic Parsing with Kernels under Various Forms of Supervision Abstract PDF
    Rohit J. Kate
  • 30. Natural Language Processing
    natural Language Processing and Computational Linguistics CLNLP95 Computational Logic for natural Language Processing Workshop on Automata Induction
    Natural Language Processing and Computational Linguistics
    Associations / Networks / Research Cooperations
    Organizations, Research Laboratories
    Universities and Academic Sites
  • Lingsoft, Inc. - Provides with linguistic tools for text retrieval and information management systems.
  • Translation Experts Limited
  • Canon Research Centre Europe, Natural Language Processing
  • 31. Cambridge Journals Online
    natural Language Engineering, Netherlands International Law Review, Netherlands Yearbook of Theatre Survey, Theory and Practice of Logic Programming

    32. Machine Learning And Natural Language Processing Lab
    This course will deal with Computational Logic, natural Language Processing, Machine Learning, and theintersections of these three areas.

    Institute for Computer Science

    Events People ... Contact
    Spezialvorlesung "Logic, Language and Learning"
    Prof. Dr. Luc De Raedt
    Co-organizer : Dr. Stefan Kramer
    Tuesday, 16-18 o'clock, Wednesday 11-12 o'clock, SR 101-00-010/014
    Exercices: Wednesday, 12-13 o'clock, SR 101-00-010/014 Credit points (Kreditpunkte): 6 Exam day: Monday February 17, 10-12 o'clock, Room: HS 101 00-026 Repetition Exam day: Wednesday April 23, 10-12 o'clock, Room: 101 01-009/013
    * NEW:
    The exam has been marked. You can obtain your marks by personally contacting the secretariat of the Machine Learning Lab in Building 79 or by phone +49 761 203 8006. This course will deal with Computational Logic, Natural Language Processing, Machine Learning, and theintersections of these three areas. The first part of the course will be devoted to Prolog, probably the most popular language for programming artificial intelligence applications. Prolog is based on first order logic. Hence, programming simply takes the form of declaring axioms in first order logic. Executing Prolog programs is then based on the orem proving. The course will introduce the basic concepts of logic programming and Prolog, and present example programs from Machine Learning and Natural Language Processing. The Prolog part of the course will largely be based on the book "Simply Logical" by Peter Flach, Wiley, 1994. The second part of the course will introduce Inductive Logic Programming (ILP), the study of Machine Learning and Data Mining within representations offered by logic programming and Prolog. In this part, we will use our own slides, but useful material on the topic can be found in the book "Relational Data Mining" edited by Saso Dzeroski and Nada Lavrac, Springer, 2001. The third part of the course will present Natural Language Processing in logic programming representations (as introduced in the first part) and ILP applications to Natural Language Processing. An introduction to Natural Language Processing (NLP) is given in "Natural Language Understanding" by James Allen, Addison-Wesley, 2nd Edition, 1995.

    33. Natural Language Theory And Technology
    This uses our Twoway Bridge between Language and Logic to provide a robust, broad-coverage mapping between natural language strings and abstract Knowledge

    34. The Grammar / Logic Divide « CHAT — Collaborative For Humanities, Art, An
    But if grammar differs from Logic because of motive — desire for a win state — and game rules, can t code come close to natural language?
    A blog about collaboration in interdisciplinary digital research projects, focusing especially upon exchanges between humanists and visual artists Previous Post inform 7, a natual language programming language
    The Grammar / Logic Divide
    I want to think more superficially than Ira about the relationship between code and natural language, so here is another set of undisciplined leaps (soon to be disciplined, as I slog through ed. by Benthem and Ter Meulen). Casey Reas iterates the standard line about the difference between code and natural language: Machine languages are very different from human languages: They are terse, have strict syntactial rules, and small vocabularies. In contrast, our languages are verbose, ambiguous, and contain huge vocabularies. ("The Language of Computers," in Creative Code , Maeda). My own experience in both programming and writing is that, when you are first learning to write them, programs approach natural language in ambiguity and verbosity. And the same is true of natural language: the more skilled you get, the more you can reduce verbosity and control ambiguity. Well, you might say, if a person is an unskilled programmer, the program doesn't work. And natural language DOES? I remember when I first read an essay by M. H. Abrams attacking J. Hillis Miller: it was written when deconstruction first came on the scene. Abrams countered Derrida and Miller with "language works!" I remember thinking to myself, that doesn't really match my daily experience. It works sometimes a lot better than others — and that's just at the level of passing information — never mind sincerity, authenticity, effectivity.

    35. Linear Logic For Natural Language Mini-colloquium
    Recent work has shown how a fragment of linear Logic can be used as a metalanguage for building up formal semantic analyses of natural language sentences.
    Linear Logic for Natural Language - An Informal Meeting
    Recent work has shown how a fragment of linear logic can be used as a meta-language for building up formal semantic analyses of natural language sentences. Other work has shown how ideas from linear logic are useful in categorial approaches to the syntax of natural language. We decided to have a meeting dedicated to the theme ``Linear logic for Natural Language" at the School of Computer Science, University of Birmingham. Talks are on Thursday 20th March, from 9:30 am till 4 o'clock pm in the Law Faculty, Faculty Board Room. Speakers are (provisionally) Dr Dick Crouch, Dr Josef van Genabith, Dr Mark Hepple and Dr Valeria de Paiva. There is a list of abstracts below. A map of the University of Birmingham campus can be found here
    A Tutorial on Linear Logic for Natural Language - Dick Crouch
    This talk will introduce some of the ideas behind the use of linear logic in natural language analysis. It will be slanted more towards the use of LL for semantic analysis, but will also touch on its use in syntax.
  • An ignoramus's overview of linear logic This will highlight some properties of linear logic that are particularly salient to its use in natural language analysis.
  • 36. Computer-based Workstation For Generation Of Logic Diagrams From Natural Languag
    Since current stateof-the art computers are unable to structure the Logic of natural language, an artificial means for doing so is required.
    United States Patent 5617578
    Computer-based workstation for generation of logic diagrams from natural language text structured by the insertion of script symbols
    US Patent Issued on April 1, 1997
    No. 655665 filed on 1996-06-03
    Current US Class
    Natural language Straight line
    Attorney, Agent or Firm
    US Patent References
      Multi-image communications system
      Issued on: September 22, 1987
      Inventor: Bedrij
      Superblock structure in a multiple in a data editor
      Issued on: February 2, 1988
      Inventor: Barker, et al.
      Method for displaying program executing circumstances and an apparatus using the same
      Issued on: October 3, 1989
      Inventor: Maezawa, et al.
      Processing apparatus for generating flow charts
      Issued on: October 17, 1989 Inventor: Smith Apparatus and process for creating variably sized block diagrams to accommodate variable text contents while yet retaining overall block shape Issued on: January 2, 1990 Inventor: Hollett Silicon-compiler method and arrangement Issued on: April 2, 1991

    37. Metalog - The PNL Interface
    The goal of the Metalog s PNL ( Pseudo natural Language ) is to define a technology that is the Metalog Logic, and on the other side, the Metalog PNL.
    The PNL interface
    (or, who ever said the Semantic Web had to be difficult?)
    The goal of the Metalog's PNL ("Pseudo Natural Language") is to define a technology that is very close to the people, even if this possibly means sacrificing part of the expressive power of the underlying tower (in other words, to start filling up the upper parts of the P axis ). The PNL, as the name says, aims to use a very colloquial form of communication, that is very close to humans: natural language. For instance, the following is a piece of an example from the Metalog distribution: JOHN and MARY OWN a "red house". As we can see, the idea is to sit high enough in the P axis , so that people can understand what this is about, without the need for reading complex manuals and getting acquainted to the computers' terminology. It is then, for one time, up to the machine to do the effort of understanding this, and push it down to some place in the underlying tower. In fact, Metalog has at its core a two-tier architecture: on the one side, the Metalog logic , and on the other side, the Metalog PNL . The Metalog logic sits in the "Logic" part of the tower , that is to say, in the expressiveness axis: it is a foundation technology, designed for the computers. On the other side of the spectrum, this level interacts with the PNL, which dually, sits above it in the

    38. Natural Language Understanding For Capturing And Understanding Business Logic
    And because it automates business Logic at the press of a button, NLU is an aspect of natural Language Processing, a formal area of computer science
    //var DOCUMENTGROUP=''; //var DOCUMENTNAME=''; //var ACTION='';
    Natural Language Understanding (NLU)
    HaleyAuthority learns the definitions of your business language and literally understands your sentences well enough to generate the programming code that implements your business process - automatically. And because it automates business logic at the press of a button, business and technical professionals can cooperatively author, test, and maintain information systems that adapt and grow with a business as its policies and practices evolve. What is Natural Language Understanding (NLU)?
    • NLU enables computers to "understand" human language, making English a programming language! NLU goes beyond words (text) and syntax (form) to include semantics (meanings). NLU enables computers to automatically generate code for terms and operations typically required in object logic, such as time, distance, mass, quantity, etc. NLU is an aspect of Natural Language Processing, a formal area of computer science technology. NLU enables users to program in a higher level language - English.

    39. COLING: , Default Logic, Natural Language ...
    Default Logic is then integrated into a theory of natural language semantics, namely Generalized Quantifiers. Finally, properties of interest to the AI

    40. JSTOR The Logic Of Natural Language.
    The Logic of natural Language. Fred Sommers. Oxford The Clar endon Press, 1982. xvii, 467 p. 19.50, $29.95. In this rich, clever, and courageous book<786:TLONL>2.0.CO;2-8

    41. [6-9] Natural Language Processing
    natural Language Processing * ALE (Attribute Logic Engine) is a freeware system written in Prolog that integrates phasestructred parsing,
    Single Page
    Top Document: Artificial Intelligence FAQ:6/6 AI Software [Monthly posting]
    Previous Document: [6-8] Knowledge Representation - Medical
    Next Document: [6-9a] Speech
    Usenet FAQs
    Search Web FAQs ... RFC Index
    [6-9] Natural Language Processing
    Natural Language Processing: * ALE (Attribute Logic Engine) is a freeware system written in Prolog that integrates phase-structred parsing, semantic-head-driven generalization and constraint logic programming with typed features such as terms. You can find ALE at The site details how to build an NLP grammar using a head-driven phase structured grammar (HPSG) and ALE. * Eric Brill's trainable rule-based part of speech tagger (version 1.0.2) is available by anonymous ftp from This tagger is based on transformation-based error-driven learning, a technique that has been effective in a number of natural language applications, including part of speech and word sense tagging, prepositional phrase attachment, and syntactic parsing. For more information, you can obtain relevant papers in

    42. A Hypothetical Reasoning Framework For Natural Language Processing
    We introduce a framework for natural language processing based on linear and intuitionistic assumptions, a new type of Logic grammars (HAGs),
    A Hypothetical Reasoning Framework for Natural Language Processing
    Andrew Fall
    Logic Programming Group,
    School of Computing Science,
    Simon Fraser University,
    Burnaby, B.C. V5A-1S6
    Paul Tarau
    Moncton, Canada E1A-3E9
    Veronica Dahl
    Logic Programming Group, School of Computing Science, Simon Fraser University, Burnaby, B.C. V5A-1S6
    We introduce a framework for natural language processing based on linear and intuitionistic assumptions, a new type of logic grammars (HAGs), and a sparse term encoding of sorts used to implement semantic taxonomies. Our framework is able to infer type relationships as well as deal with backtrackable state information in a principled yet highly efficient way. Intuitionistic and linear implications scoped over the current continuation allow us to follow given branches of the computation under hypotheses that disappear when and if backtracking takes place. Unlike previous similar frameworks, ours maintains high-level descriptiveness through the use of hidden multiple accumulators . This new technique offers the equivalent of EDCGs [ ] without the need for a preprocessor.

    43. Blackburn, Patrick: Representation And Inference For Natural Language
    Firstorder Logic First-order Logic Three inference tasks A first-order model checker First-order Logic and natural language 2. Lambda calculus
    The University
    of Chicago Press
    Home Shopping Cart
    Our books:
    Also @ Chicago:

    Print an order form
    Blackburn, Patrick and Johan Bos Representation and Inference for Natural Language A First Course in Computational Semantics . Distributed for the Center for the Study of Language and Information. 376 p. 6 x 9 2005 Series: (CSLI-SCL) Center for the Study of Language and Information - Studies in Computational Linguistics Cloth $70.00sc ISBN: 978-1-57586-495-2 (ISBN-10: 1-57586-495-9) Fall 2005
    Paper $30.00sp ISBN: 978-1-57586-496-9 (ISBN-10: 1-57586-496-7) Fall 2005
    How can computers distinguish the coherent from the unintelligible, recognize new information in a sentence, or draw inferences from a natural language passage? Computational semantics is an exciting new field that seeks answers to these questions, and this volume is the first textbook wholly devoted to this growing subdiscipline. The book explains the underlying theoretical issues and fundamental techniques for computing semantic representations for fragments of natural language. This volume will be an essential text for computer scientists, linguists, and anyone interested in the development of computational semantics. TABLE OF CONTENTS 1. First-order logic

    44. BCIG Links And Resources
    on fuzzy Logic, soft computing, computing with words, and the newly developed computational theory of perceptions and precisiated natural language.
    home about bcig past event archives member collaboration ... links
    BCIG SPEAKER EVENT: “A New Frontier in Computation - Computation with Information Described In Natural Language”
    Clinical Center (Building 10) Medical Board Room (Room 2C116) - view the seminar archive DESCRIPTION
    3:00 - 4:30 pm February, 2007
    Professor Lotfi A. Zadeh
    Professor of Computer Science
    University of California, Berkeley Lotfi A. Zadeh joined the Department of Electrical Engineering at the University of California, Berkeley, in 1959, and served as its chairman from 1963 to 1968. Earlier, he was a member of the electrical engineering faculty at Columbia University. In 1956, he was a visiting member of the Institute for Advanced Study in Princeton, New Jersey. In addition, he held a number of other visiting appointments, among them a visiting professorship in Electrical Engineering at MIT in 1962 and 1968; a visiting scientist appointment at IBM Research Laboratory, San Jose, CA, in 1968, 1973, and 1977; and visiting scholar appointments at the AI Center, SRI International, in 1981, and at the Center for the Study of Language and Information, Stanford University, in 1987-1988. Currently he is a Professor in the Graduate School, and is serving as the Director of BISC (Berkeley Initiative in Soft Computing). Until 1965, Dr. Zadeh's work had been centered on system theory and decision analysis. Since then, his research interests have shifted to the theory of fuzzy sets and its applications to artificial intelligence, linguistics, logic, decision analysis, control theory, expert systems and neural networks. Currently, his research is focused on fuzzy logic, soft computing, computing with words, and the newly developed computational theory of perceptions and precisiated natural language.

    45. Episodic Logic And EPILOG
    Episodic Logic is a knowledge representation developed for use as a semantic theory for natural language understanding. EPILOG is the computational system
    Episodic Logic is a knowledge representation developed for use as a semantic theory for natural language understanding. EPILOG is the computational system for Episodic Logic (EL). It is a powerful knowledge management and inference system. The EPILOG family has been under development at the University of Alberta and University of Rochester for over twenty years, with the financial support from the Boeing Co. in Seattle during 1987-1992. (The first delivery of the EPILOG system was in 1990.) EPILOG is now available for download. Page content:
  • Episodic Logic EPILOG System EPILOG Sample Output Download EPILOG ... Publications
  • Authors:
    • Lenhart K. Schubert Stephanie Schaeffer (developed most of the EPILOG code and documentation) Chung Hee Hwang (developed much of EL and contributed to EPILOG) Johannes de Haan (contributed to EPILOG and the documentation) Aaron Kaplan (contributed to EPILOG and the documentation) Fabrizio Morbini (currently handling bug registry and repairs)
    For further information please email to: epilog AT cs DOT rochester DOT edu
    Episodic Logic (EL)
    The knowledge representation Episodic Logic EL ) was developed for use as a semantic representation for natural language understanding, supporting general inference. It was designed to meet the following requirements:

    46. What Are Logic Programming And Prolog?
    Logic programming with an emphasis on natural language processing (aka grammars). Prolog and natural Language Analysis , Fernando C. N. Pereira and Stuart
    What are Logic Programming and Prolog?
    There are many ways of organizing computations. Perhaps the most familiar paradigm is procedural : the program specifies a computation by saying how it is to be performed. FORTRAN, C, and even object-oriented languages fall under this general approach. Another paradigm is declarative
    The Merits of Declarative Languages
    The use of declarative languages offers three important advantages.
    • Logic programming languages are inherently ``high-level'' because they focus on the computation's logic and not on its mechanics (which in fact are inaccessible to the programmer). The result is that they are well-suited to expressing complex ideas because the drudgery of memory management, stack pointers, etc., is left to the computational engine. Since the engine incorporates logical inferencing, it is already a powerful tool which can be exploited in developing inference engines specific to a particular universe of discourse (sometimes also called a domain ). For

    47. Natural Language Speech Recognition | Text To Speech | Natural
    natural Language Speech Recognition has the ability to adapt to the callers request and make real time adjustments by using built in Logic.
    reconnaissance vocale Natural Language Speech Recognition Vocalcom provide Cost effective turn key solutions for the Contact Center Natural Language is the only ... speech recognition technology Natural Language Speech Recognition
    EN ES FR ... Client Area Applications Products Technology Company Download Home
    Natural Language
    Natural Language Speech Recognition
    Natural Language is the only speech recognition engine all over the world which manages voice and chat dialogues with capability of changing the language during the conversation.
    Are you a provider that is interested in being Natural language certified ? Vocalcom is actively looking for Business Partners. Contact us to find out more !
    Natural Language
    Speech Recognition 's
    Key Features
    Software Architecture - World exclusivity Multilanguage even during conversation Natural Voice Recognition Grammar and syntax analysis Artificial intelligence dialogue engine Text to Speech Text to Speech Voice and Chat Dialogue Connect to Standard databases Real time modifications Test and tuning mode before running Send Emails Send SMS Native CTI link/p> Available API Standard and personalized statistics
    Answer your clients calls 24/7
    X'Voice is your virtual assistant. Today's speech enabled products are sophisticated and very powerful allowing you to personalize and transmit the information in your databases to your callers in real time.

    48. APL2C: Persian Speech And Natural Language Processing Home Page
    Design and Implementation of a natural Language Shell for Relational Data Bases of Logic and Computation; SCHOLAR, natural Language Processing On Line
    Speech and Natural Language Processing (NLP)
    Software for Persian Langugae
    Relevant News and Conferences
    Directory of People interested in Persian NLP [to appear]
    Papers, Books and Bibliographies on NLP and Speech

    49. LINGUIST List 18.3688: General Ling/USA; Computational Ling,Pragmatics/Japan
    Directory 1. Julia Kuznetsova, Formal Approaches to Slavic Linguistics 17 2. Eric McCready, Logic and Engineering of natural Language Semantics 5
    LINGUIST List 18.3688
    Mon Dec 10 2007
    Calls: General Ling/USA; Computational Ling,Pragmatics/Japan
    As a matter of policy, LINGUIST discourages the use of abbreviations or acronyms in conference announcements unless they are explained in the text. To post to LINGUIST, use our convenient web form at Directory
    1. Julia Kuznetsova, Formal Approaches to Slavic Linguistics 17
    2. Eric McCready, Logic and Engineering of Natural Language Semantics 5
    Message 1: Formal Approaches to Slavic Linguistics 17 Date: 07-Dec-2007
    Subject: Formal Approaches to Slavic Linguistics 17
    E-mail this message to a friend
    Full Title: Formal Approaches to Slavic Linguistics 17 Short Title: FASL 17 Date: 02-May-2008 - 04-May-2008 Location: New Haven, CT, USA Contact Person: Jodi Reich Meeting Email: jodi.reich Linguistic Field(s): General Linguistics Call Deadline: 15-Jan-2008 Meeting Description The 17th annual meeting of Formal Approaches to Slavic Linguistics (FASL-17) will be held at Yale University May 2-4, 2008.

    50. Computational Linguistics At CWI ­p; The Logic Of Ambiguity
    CWI has been involved in a largescale national project on the application of tools from dynamic Logic to natural language understanding, in a European
    subscribe back issues on-line order back issues advertise ... ERCIM web site
    ERCIM News No.26 - July 1996 - CWI
    Computational Linguistics at CWI - the Logic of Ambiguity
    by Jan van Eijck
    Computational Linguistics combines insights from formal language theory, empirical linguistics, and logic, with the overall aim to implement natural language understanding systems on computers. A group of applied logicians at CWI has looked at the logical underpinnings of computational linguistic tools such as semantic representation languages, feature logics, and tree description logics.
    The work on dynamic logic has resulted in a proposal for a framework for dynamic semantics, in an analysis of Discourse Representation Theory in terms of dynamic logic, and in various publications on modal tree logics. One of the yields of the FraCaS work has been an analysis of the logic of ambiguity by J. van Eijck and J. Jaspars. To this we now turn.
    In the formal study of natural language semantics the representation of ambiguous information is one of the major problems. Initial representations of NL expressions are often ambiguous, due to lack of information about the meanings of lexical items (lexical ambiguity), the ways in which anaphoric elements are to be resolved (anaphoric under-specification), attachment ambiguities (structural ambiguity) and the choice between various possible scope orderings between operators (scope ambiguity).
    The principal reason for wanting to construct a meaning representation for a natural language sentence is to get a handle on the information conveyed by that sentence. Is the sentence consistent with a given body of information? If the sentence is true, what follows from it? If a natural language sentence is ambiguous, as many natural language sentences are, the key question becomes: how can we find a representation for it that we can reason with?

    51. Flairs 06: Natural Language And Knowledge Representation
    We believe that the natural Language Processing (NLP) and the Knowledge Representation (KR) communities have common goals. They are both concerned with
    Natural Language and Knowledge Representation
    objective and topics program committee invited speakers journal issue ... FLAIRS 06: Melbourne Beach, Florida May 11-13, 2006
    We believe that the Natural Language Processing (NLP) and the Knowledge Representation (KR) communities have common goals. They are both concerned with representing knowledge and with reasoning, since the best test for the semantic capability of an NLP system is performing reasoning tasks. Having these two essential common grounds, the two communities ought to have been collaborating, to provide a well-suited representation language that covers these grounds. However, the two communities also have difficult-to-meet concerns. Mainly, the semantic representation (SR) should be expressive enough and take the information in context into account, while the KR should be equipped with a fast reasoning process. The main objection against using an SR or a KR is that they need experts to be understood. Non-experts communicate (usually) via a natural language (NL), and more or less they understand each other while performing a lot of reasoning. An essential practical value of representations is their attempt to be transparent. This will particularly be useful when/if the system provides a justification for a user or a knowledge engineer on its line of reasoning using the underlying KR (i.e. without generating back to NL). We all seem to believe that, compared to Natural Language, the existing Knowledge Representation and reasoning systems are poor. Nevertheless, for a long time, the KR community has dismissed the idea that NL can be a KR. That's because NL can be very ambiguous and there are syntactic and semantic processing complexities associated with it. However, researchers in both communities have started looking at this issue again. Possibly, it has to do with the NLP community making some progress in terms of processing and handling ambiguity, the KR community realising that a lot of knowledge is already 'coded' in NL and that one should reconsider the way they handle expressivity and ambiguity.

    52. RealDialog - Intelligent, Natural Language Search, Web Self-Service, Customer Se
    Through advanced linguistic processing and natural language understanding, it provides them consistent, timely, and accurate information about your products
    Company Solutions Industries Services ...

    • Intelligent, Natural Language Search Superior Web Self-Service for Customers, Employees and Partners Streamlined Knowledge Management Contact Center Agent Assist and Knowledge Base Intelligent Email and Chat Response Management "Voice of the Customer" Analysis and Reporting
    RealDialog is a compelling knowledge management and Web self-service solution for your consumers, contact center agents, and employees. Through advanced linguistic processing and natural language understanding, it provides them consistent, timely, and accurate information about your products, services and policies across all communication channels. With RealDialog, users can simply type a question in the manner they prefer and receive an immediate, accurate and personalized response without the need to drill down through pages of search results or FAQs to find the answer they need. Because RealDialog has an extensive understanding of the English language, it is able to interact with users in a manner that mimics a friendly "live" agent, conducting a "dialog" and asking questions as appropriate. All the while, RealDialog captures the actual questions users are asking, providing companies direct and unfiltered insight into customer needs and concerns.

    53. Nasslli Home Page --
    The main focus of NASSLLI is on the interface between linguistics, Logic, They are Mathematics of Language, and Theoretical Aspects of Reasoning About
    Home Program Links Sponsors ... Contact us
    The main focus of NASSLLI is on the interface between linguistics, logic, and computation, broadly conceived, and on related fields. NASSLLI is a week-long summer school featuring courses on many topics of interest to students and researchers. Some of the course topics are introductory, while others are advanced courses that bring students to areas of active research. The instructors are leading researchers who like teaching in interdisciplinary settings. Three of the courses involve work in computer labs as well. NASSLLI also has a student session , as well evening lectures and social events. This year, two conferences overlap with NASSLLI. They are Mathematics of Language , and Theoretical Aspects of Reasoning About Knowledge . Both MoL and TARK will be held June 20-22. NASSLLI participants are encouraged to attend the lectures of these conferences. Please come to NASSLLI for an exciting week of learning this coming June. Please send all correspondence to

    Page 1     1-58 of 58    1