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The Department of Computer Science & Engineering
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CSE 740: SEMINAR:
CONTEXTUAL VOCABULARY ACQUISITION (Spring 2004) |
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| Instructor: | Prof. William J. Rapaport |
| Times: | Fridays 1:00 - 3:30 p.m. |
| Classroom: | Bell 224 |
Course Description:
This seminar will be devoted to a research project being conducted by
Prof. William J. Rapaport (Department of
Computer Science and Engineering, and Center for Cognitive Science)
and
Prof. Michael W. Kibby
(Department of Learning and Instruction, and
Center for Literacy and Reading Instruction):
We are developing a computational theory of how natural-language-understanding systems can automatically acquire new vocabulary by determining from context the meaning of words that are unknown, misunderstood, or used in a new sense, and adapting the algorithms for doing this to a curriculum so that these methods can be taught to students in a classroom setting.
We propose:
(a) to extend and develop algorithms for computational contextual vocabulary acquisition (CVA): learning, from context, meanings for "hard" word: nouns (including proper nouns), verbs, adjectives, and adverbs,
(b) to unify a disparate literature on the topic of CVA from psychology, first- and second-language (L1 and L2) acquisition, and reading science, in order to help develop these algorithms, and
(c) to use the knowledge gained from the computational CVA system to build and to evaluate the effectiveness of an educational curriculum for enhancing students' abilities to use deliberate (i.e., non-incidental) CVA strategies in their reading of science, math, engineering, and technology texts at the middle-school and college undergraduate levels: teaching methods and guides, materials for teaching and practice, and evaluation instruments.
The knowledge gained from case studies of students using our CVA techniques will feed back into further development of our computational theory.
The seminar will involve reading research literature on CVA from computational linguistics, psychology, and education; using the SNePS knowledge representation and reasoning system, and/or using natural-language-processing techniques such as ATN (augmented-transition-network) grammars.
Prerequisites:
Graduate standing, or permission of instructor.
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