David Bourget (Western Ontario)
David Chalmers (ANU, NYU)
Rafael De Clercq
Jack Alan Reynolds
Learn more about PhilPapers
cal practice: the enterprise of specifying information about the world for use in computer systems. Knowledge representation as a ﬁeld also encompasses conceptual results that call practitioners’ attention to important truths about the world, mathematical results that allow practitioners to make these truths precise, and computational results that put these truths to work. This chapter surveys this practice and its results, as it applies to the interpretation of natural language utterances in implemented natural language processing systems. For a broader perspective on such technical practice, in all its strengths and weaknesses, see (Agre 1997). Knowledge representation offers a powerful general tool for the science of language. Computational logic, a prototypical formalism for representing knowledge about the world, is also the model for the level of logical form that linguists use to characterize the grammar of meaning (Larson and Segal 1995). And researchers from (Schank and Abelson 1977) to (Shieber 1993) and (Bos to appear) have relied crucially on such representations, and the inference methods associated with them, in articulating accounts of semantic relations in language, such as synonymy, entailment, informativeness and contradiction. The new textbooks (Blackburn and Bos 2002a, Blackburn and Bos 2002b) provide an excellent grounding in this research, and demonstrate how deeply computational ideas from knowledge representation can inform pure linguistic study. In this short chapter, I must leave much of..
|Keywords||No keywords specified (fix it)|
|Categories||categorize this paper)|
Setup an account with your affiliations in order to access resources via your University's proxy server
Configure custom proxy (use this if your affiliation does not provide a proxy)
|Through your library||
References found in this work BETA
No references found.
Citations of this work BETA
No citations found.
Similar books and articles
Patrick Blackburn & Johan Bos (2003). Computational Semantics. Theoria 18 (1):27-45.
Lucja Iwańska (1993). Logical Reasoning in Natural Language: It is All About Knowledge. [REVIEW] Minds and Machines 3 (4):475-510.
Patrick Blackburn (2005). Representation and Inference for Natural Language: A First Course in Computational Semantics. Center for the Study of Language and Information.
Johan Bos (2004). Computational Semantics in Discourse: Underspecification, Resolution, and Inference. Journal of Logic, Language and Information 13 (2):139-157.
William J. Rapaport, Erwin M. Segal, Stuart C. Shapiro, David A. Zubin, Gail A. Bruder, Judith Felson Duchan & David M. Mark, Cognitive and Computer Systems for Understanding Narrative Text.
Martha Stone Palmer (2006). Semantic Processing for Finite Domains. Cambridge University Press.
Barry C. Smith (2006). What We Know When We Know a Language. In Ernest Lepore & Barry C. Smith (eds.), Oxford Handbook of Philosophy of Language. Oup Oxford.
Barry C. Smith (2006). What I Know When I Know a Language. In Ernest Lepore & Barry C. Smith (eds.), The Oxford Handbook of Philosophy of Language. Oxford University Press.
Charles E. M. Dunlop (1990). Conceptual Dependency as the Language of Thought. Synthese 82 (2):275-96.
Syed S. Ali & Stuart C. Shapiro (1993). Natural Language Processing Using a Propositional Semantic Network with Structured Variables. Minds and Machines 3 (4):421-451.
Chris Fox (2005). Foundations of Intensional Semantics. Blackwell Pub..
Michael Levison (2012). The Semantic Representation of Natural Language. Bloomsbury Academic.
Added to index2010-12-22
Total downloads4 ( #299,570 of 1,692,743 )
Recent downloads (6 months)2 ( #108,992 of 1,692,743 )
How can I increase my downloads?