David Bourget (Western Ontario)
David Chalmers (ANU, NYU)
Rafael De Clercq
Jack Alan Reynolds
Learn more about PhilPapers
��We describe a comprehensive framework for text un- derstanding, based on the representation of context. It is designed to serve as a representation of semantics for the full range of in- terpretive and inferential needs of general natural language pro- cessing. Its most distinctive feature is its uniform representation of the various simple and independent linguistic sources that play a role in determining meaning: lexical associations, syntactic re- strictions, case-role expectations, and most importantly, contextual effects. Compositional syntactic structure from a shallow parsing is represented in a neural net-based associative memory, where it then interacts through a Bayesian network with semantic associa- tions and the context or “gist” of the passage carried forward from preceding sentences. Experiments with more than 2000 sentences in different languages are included.
|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
James Franklin & S. W. K. Chan (2003). Dynamic Context Generation for Natural Language Understanding: A Multifaceted Knowledge Approach. IEEE Transactions on Systems, Man and Cybernetics Part A 33:23-41.
Samuel W. K. Chan & James Franklin (1998). Symbolic Connectionism in Natural Language Disambiguation. IEEE Transactions on Neural Networks 9:739-755.
Lucja Iwańska (1993). Logical Reasoning in Natural Language: It is All About Knowledge. [REVIEW] Minds and Machines 3 (4):475-510.
Matthew Stone & Richmond H. Thomason, Coordinating Understanding and Generation in an Abductive Approach.
Martha Stone Palmer (2006). Semantic Processing for Finite Domains. Cambridge University Press.
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.
Xiaoxi Huang, Huaxin Huang, Beishui Liao & Cihua Xu (2013). An Ontology-Based Approach to Metaphor Cognitive Computation. Minds and Machines 23 (1):105-121.
James Franklin (2003). The Representation of Context: Ideas From Artiﬁcial Intelligence. Law, Probability and Risk 2:191-199.
Jan Eijck & Fer-Jan Vries (1992). Dynamic Interpretation and HOARE Deduction. Journal of Logic, Language and Information 1 (1):1-44.
William J. Rapaport (1988). Syntactic Semantics: Foundations of Computational Natural Language Understanding. In James H. Fetzer (ed.), Aspects of AI. Kluwer.
Kirsten Malmkjær & John Williams (eds.) (1998). Context in Language Learning and Language Understanding. Cambridge University Press.
Dan Klein & Christopher D. Manning, Natural Language Grammar Induction Using a Constituent-Context Model.
Jon Williamson (2006). Introduction. Journal of Logic, Language and Information 15 (1-2):1-3.
Added to index2010-12-22
Total downloads3 ( #345,547 of 1,692,590 )
Recent downloads (6 months)1 ( #181,202 of 1,692,590 )
How can I increase my downloads?