(Computational) Contextual Vocabulary Acquisition

1. Rapaport, William J., & Ehrlich, Karen (2000), "A Computational Theory of Vocabulary Acquisition", in Lucja M. Iwanska & Stuart C. Shapiro (eds.), Natural Language Processing and Knowledge Representation: Language for Knowledge and Knowledge for Language (Menlo Park, CA/Cambridge, MA: AAAI Press/MIT Press): 347-375.

   Abstract: As part of an interdisciplinary project to develop a computational cognitive model of a reader of narrative text, 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. `Context' includes surrounding text, grammatical information, and background knowledge, but no external sources. Our thesis is that the meaning of such a word can be determined from context, can be revised upon further encounters with the word, "converges" to a dictionary-like definition if enough context has been provided and there have been enough exposures to the word, and eventually "settles down" to a "steady state" that is always subject to revision upon further encounters with the word. The system is being implemented in the SNePS knowledge-representation and reasoning system.

2. Rapaport, William J., & Kibby, Michael W. (2001), "Contextual Vocabulary Acquisition: Development of a Computational Theory and Educational Curriculum: Description of Revised Scope of Project for Pilot-Project Purposes"

   • NSF pilot-project proposal.

   • 
     No doubt you have on occasion read some text containing an unfamiliar word, but you were unable or unwilling to find out from a dictionary or another person what the word meant. Nevertheless, you might, consciously or not, have figured out a meaning for it. Suppose you didn't, or suppose your hypothesized meaning was wrong. If you never see the word again, it may not matter. However, if the text you were reading were from science, mathematics, engineering, or technology (SMET), not understanding the unfamiliar term might seriously hinder your subsequent understanding of the text. If you do see the word again, you will have an opportunity to revise your hypothesis about its meaning. The more times you see the word, the better your definition will become. And if your hypothesis development were deliberate, rather than "incidental", your command of the new word would be stronger. 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 SMET 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. This project falls within Quadrant 2 (fundamental research on behavioral, cognitive, affective and social aspects of human learning) and Quadrant 3 (research on SMET learning in formal and informal educational settings).

3. 
   Rapaport, William J., & Kibby, Michael W. (2002), "Contextual Vocabulary Acquisition: A Computational Theory and Educational Curriculum", in Nagib Callaos, Ana Breda, and Ma. Yolanda Fernandez J. (eds.), Proceedings of the 6th World Multiconf erence on Systemics, Cybernetics and Informatics (SCI 2002; Orlando, FL) (Orlando: International Institute of Informatics and Systemics), Vol. II: Concepts and Applications of Systemics, Cybernetics, and Informatics I, pp. 261-266.

   Abstract: We discuss a research project that develops and applies algorithms for computational contextual vocabulary acquisition (CVA): learning the meaning of unknown words from context. We try to unify a disparate literature on the topic of CVA from psychology, first- and second-language acquisition, and reading science, in order to help develop these algorithms: We use the knowledge gained from the computational CVA system to build an educational curriculum for enhancing students' abilities to use CVA strategies in their reading of science texts at the middle-school and college undergraduate levels. The knowledge gained from case studies of students using our CVA techniques feeds back into further development of our computational theory.

   • 
     Slide show: (powerpoint) (html)

4. Older slide show
   • pdf
   • ps
   • dvi

5. Bibliography of Contextual Vocabulary Acquisition

6. The "brachet" demo:
   The original "brachet" story.
   Also available in full at:
   • Malory's Morte D'Arthur
   • Malory's Morte D'Arthur, part I
   • Malory's Morte D'Arthur, part II
   • The plain-English "brachet" story.

   • The background knowledge for the "brachet" story.

   • The brachet demo (and others)

7. More CVA passages

8. List of websites that might be useful in finding passages with target words

9. List of target words for secondary students

10. List of target words for middle school students

11. Karen Ehrlich's original CVA algorithm (in SNePSUL)

12. The Ehrlich algorithm in English (by Scott T. Napieralski)

13. Streamlined version of the Ehrlich algorithm in English (by William J. Rapaport, Marc K. Broklawski, and Scott T. Napieralski

14. More efficient version of the Ehrlich algorithm (by Marc K. Broklawski).

15. Instructions for Running CVA (Noun) Algorithm and SNePS on UB CSE Machines

16. Marc's CVA directory
    • Only available from UB/CSE machines

17. Bill's CVA directory
    • Only available from UB/CSE machines

18. A Dictionary of CVA SNePS Case Frames

19. Progress Reports

    • "estuary" (work by Matt Sweeney)

    • "proliferate" (work by Marc Broklawski and Justin Del Vecchio)
      • report #1
      • report #2
      • report #3
      • report #4
      • final report

    • "proximity" (work by Valerie Yakich and Scott Napieralski)

      • final report (PDF)

      • Scott's CVA webpage

    • "taciturn" (work by, in chronological order, Kazuhiro Kawachi, Christopher J. Garver, and Adam Lammert)
      • Garver's work
      • Lammert's work
        • full report in plain text
        • interaction in plain text

    • verb algorithm (work by Rajeev Sood and Justin Del Vecchio)

      • Sood's final report (PDF)

      • 
        Del Vecchio's webpage

    • noun algorithm (work by Scott Napieralski)

20. CSE 663 Accomplishments (slide show)
    • (powerpoint)
    • (pdf)

21. Marc Broklawski's CVA resources page