David Bourget (Western Ontario)
David Chalmers (ANU, NYU)
Rafael De Clercq
Jack Alan Reynolds
Learn more about PhilPapers
In Anind Dey, Boicho Kokinov, David Leake & Roy Turner (eds.), Proceedings of the 5th International and Interdisciplinary Conference on Modeling and Using Context. Springer-Verlag Lecture Notes in Artificial Intelligence 3554. 396--409 (2005)
Contextual vocabulary acquisition (CVA) is the deliberate acquisition of a meaning for a word in a text by reasoning from context, where “context” includes: (1) the reader’s “internalization” of the surrounding text, i.e., the reader’s “mental model” of the word’s “textual context” (hereafter, “co-text” ) integrated with (2) the reader’s prior knowledge (PK), but it excludes (3) external sources such as dictionaries or people. CVA is what you do when you come across an unfamiliar word in your reading, realize that you don’t know what it means, decide that you need to know what it means in order to understand the passage, but there is no one around to ask, and it is not in the dictionary (or you are too lazy to look it up). In such a case, you can try to ﬁgure out its meaning “from context”, i.e., from clues in the co-text together with your prior knowledge. Our computational theory of CVA—implemented in a the SNePS knowledge representation and reasoning system —begins with a stored knowledge base containing SNePS representations of relevant PK, inputs SNePS representations of a passage containing an unfamiliar word, and draws inferences from these two (integrated) information sources. When asked to deﬁne the word, deﬁnition algorithms deductively search the resulting network for information of the sort that might be found in a dictionary deﬁnition, outputting a deﬁnition frame whose slots are the kinds of features that a deﬁnition might contain (e.g., class membership, properties, actions, spatio-temporal information, etc.) and whose slot-ﬁllers contain information gleaned from the network [6–8,20,23,24]. We are investigating ways to make our system more robust, to embed it in a naturallanguage-processing system, and to incorporate morphological information. Our research group, including reading educators, is also applying our methods to the develop-
|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
William J. Rapaport (2003). What Is the “Context” for Contextual Vocabulary Acquisition? Proceedings of the 4th Joint International Conference on Cognitive Science/7th Australasian Society for Cognitive Science Conference 2:547-552.
William J. Rapaport & Michael W. Kibby, Contextual Vocabulary Acquisition: From Algorithm to Curriculum.
William J. Rapaport & Michael W. Kibby (2002). Contextual Vocabulary Acquisition: A Computational Theory and Educational Curriculum. In Nagib Callaos, Ana Breda & Ma Yolanda Fernandez J. (eds.), Proceedings of the 6th World Multiconference on Systemics, Cybernetics and Informatics. International Institute of Informatics and Systemics.
Stuart C. Shapiro & William J. Rapaport (1992). The SNePS Family. Computers and Mathematics with Applications 23:243-275.
Koenraad Kuiper (2006). Knowledge of Language and Phrasal Vocabulary Acquisition. Behavioral and Brain Sciences 29 (3):291-292.
Marc W. Howard, Karthik H. Shankar & Udaya K. K. Jagadisan (2011). Constructing Semantic Representations From a Gradually Changing Representation of Temporal Context. Topics in Cognitive Science 3 (1):48-73.
Frank C. Keil (2001). Good Intentions and Bad Words. Behavioral and Brain Sciences 24 (6):1110-1111.
Samuel W. K. Chan & James Franklin (1998). Symbolic Connectionism in Natural Language Disambiguation. IEEE Transactions on Neural Networks 9:739-755.
William J. Rapaport (1991). Predication, Fiction, and Artificial Intelligence. Topoi 10 (1):79-111.
Marie-Francine Moens (2001). Innovative Techniques for Legal Text Retrieval. Artificial Intelligence and Law 9 (1):29-57.
Arthur R. Jensen (2001). Vocabulary and General Intelligence. Behavioral and Brain Sciences 24 (6):1109-1110.
Afsaneh Fazly, Afra Alishahi & Suzanne Stevenson (2010). A Probabilistic Computational Model of Cross-Situational Word Learning. Cognitive Science 34 (6):1017-1063.
Added to index2010-12-22
Total downloads55 ( #32,535 of 1,140,267 )
Recent downloads (6 months)6 ( #32,080 of 1,140,267 )
How can I increase my downloads?