- (Cardie, 1993) ⇒ Claire Cardie. (1993). “A Case-based Approach to Knowledge Acquisition for Domain-Specific Sentence Analysis.” In: Proceedings of the Eleventh National Conference on Artificial Intelligence (AAAI 1993).
Subject Headings: Information Extraction Algorithm
- Cardie's system required the user to create taxonomies of word senses and concept types for a set of training sentences. These taxonomies, along with part of speech tags, were used to create definitions comprising 39 attributes for each instance of a word in the training sentences. Machine learning methods were then used to determine which attributes were most important for the extraction of new definitions, and for the actual extraction of the definitions.
- This paper describes a case-based approach to knowledge acquisition for natural language systems that simultaneously learns part of speech, word sense, and concept activation knowledge for all open class words in a corpus. The parser begins with a lexicon of function words and creates a case base of context-sensitive word deﬁnitions during a humansupervised training phase. Then, given an unknown word and the context in which it occurs, the parser retrieves definitions from the case base to infer the word’s syntactic and semantic features. By encoding context as part of a deﬁnition, the meaning of a word can change dynamically in response to surrounding phrases without the need for explicit lexical disambiguation heuristics. Moreover, the approach acquires all three classes of knowledge using the same case representation and requires relatively little training and no hand-coded knowledge acquisition heuristics. We evaluate it in experiments that explore two of many practical applications of the technique and conclude that the case-based method provides a promising approach to automated dictionary construction and knowledge acquisition for sentence analysis in limited domains. In addition, we present a novel case retrieval algorithm that uses decision trees to improve the performance of a k-nearest neighbor similarity metric.
- Berwick, R. (1983). Learning word meanings from examples. Proceedings, Eighth International Joint Conference on Artificial Intelligence. Karlsruhe, Germany, pp. 459-461.
- Brent, M. (1991). Automatic acquisition of subcategorization frames from untagged text. Proceedings, 29th Annual Meeting of the Association for Computational Linguistics. University of California, Berkeley, Association for Computational Linguistics, pp. 209-214.
- Brent, M. (1990). Semantic classification of verbs from their syntactic contexts: automated lexicography with implications for child language acquisition. Proceedings, Twelfth Annual Conference of the Cognitive Science Society. Cambridge, MA, The Cognitive Science Society, pp. 428-437.
- Cardie, C. (1993). Using Decision Trees to Improve Case-based Learning. To appear in, P. Utgoff (Ed.), Proceedings, Tenth International Conference on Machine Learning. University of Massachusetts, Amherst, MA.
- Cardie, C. (1992). Learning to Disambiguate Relative Pronouns. Proceedings,Tenth National Conference on Artificial Intelligence. San Jose, CA, AAAI Press/MIT Press, pp. 38-43.
- Church, K., & Hanks, P. (1990). Word association norms, mutual information, and lexicography. Computational Linguistics, 16.
- Granger, R. 1977. Foulup: A program that figures out meanings of words from context. Proceedings, Fifth International Joint Conference on Artificial Intelligence. Morgan Kaufmann, pp. 172- 178.
- Gregory Grefenstette (1992). SEXTANT: Exploring unexplored contexts for semantic extraction from syntactic analysis. Proceedings, 30th Annual Meeting of the Association for Computational Linguistics. University of Delaware, Newark, DE, Association for Computational Linguistics, pp. 324-326.
- Hastings, P., Lytinen, S., & Lindsay, R. (1991). Learning Words from Context. Proceedings, Eighth International Conference on Machine Learning. Northwestern University, Chicago, IL.
- Hindle, D. (1990). Noun classification from predicate-argument structures. Proceedings, 28th Annual Meeting of the Association for Computational Linguistics. University of Pittsburgh, Association for Computational Linguistics, pp. 268-275.
- Jacobs, P., & Zernik, U. (1988). Acquiring Lexical Knowledge from Text: A Case Study. Proceedings, Seventh National Conference on Artificial Intelligence. St. Paul, MN, Morgan Kaufmann, pp. 739-744.
- Lehnert, W. (1990). Symbolic/Subsymbolic Sentence Analysis: Exploiting the Best of Two Worlds. In J. Barnden, & J. Pollack (Eds.), Advances in Connectionist and Neural Computation Theory. Norwood, NJ, Ablex Publishers, pp. 135-164.
- Lehnert, W., Cardie, C., Fisher, D., Ellen Riloff, & Williams, R. 1991a. University of Massachusetts: Description of the CIRCUS System as Used for MUC-3. Proceedings, Third Message Understanding Conference (MUC-3). San Diego, CA, Morgan Kaufmann, pp. 223-233.
- Lehnert, W., Cardie, C., Fisher, D., Ellen Riloff, & Williams, R. 1991b. University of Massachusetts: MUC-3 Test Results and Analysis. Proceedings,Third MessageUnderstandingConference (MUC-3). San Diego, CA, Morgan Kaufmann, pp. 116-119.
- Lytinen, S., & Roberts, S. (1989). Lexical Acquisition as a By Product of Natural Language Processing. Proceedings, IJCAI-89 Workshop on Lexical Acquisition. Proceedings, Fourth Message Understanding Conference (MUC-4). (1992).
- McLean, VA, Morgan Kaufmann. Proceedings, Third Message Understanding Conference (MUC-3). (1991). San Diego, CA, Morgan Kaufmann. J. Ross Quinlan (1992). C4.5: Programs for Machine Learning. Morgan Kaufmann.
- J. Ross Quinlan (1986). Induction of decision trees. Machine Learning, 1, pp. 81-106.
- Philip Resnik (1992). A class-based approach to lexical discovery. Proceedings, 30th Annual Meeting of the Association for Computational Linguistics. University of Delaware, Newark, DE, Association for Computational Linguistics, pp. 327-329.
- Selfridge, M. (1986). A computer model of child language learning. Artificial Intelligence, 29, pp. 171-216.
- Wilensky, R. (1991). Extending the Lexicon by Exploiting Subregularities. Tech. Report No. UCB/CSD 91/618. Computer Science Division (EECS), University of California, Berkeley.
- Yarowsky, D. (1992). Word-Sense Disambiguation Using Statistical Models of Roget’s Categories Trained on Large Corpora. Proceedings, COLING-92.
- Zernik, U. (1991). Train1 vs. Train 2: Tagging Word Senses in Corpus. In U. Zernik (Ed.), Lexical Acquisition: Exploiting On-Line Resources to Build a Lexicon. Hillsdale, NJ, Lawrence Erlbaum Associates, pp. 91-112.,
|1993 CaseBasedApproachToKnowledgeAcquisition||Claire Cardie||A Case-based Approach to Knowledge Acquisition for Domain-Specific Sentence Analysis||Proceedings of the Eleventh National Conference on Artificial Intelligence||http://mtgroup.ict.ac.cn/~sunjian/IE/cardie aaai93.pdf||1993|
Facts about "1993 CaseBasedApproachToKnowledgeAcquisition"
|Author||Claire Cardie +|
|journal||Proceedings of the Eleventh National Conference on Artificial Intelligence +|
|title||A Case-based Approach to Knowledge Acquisition for Domain-Specific Sentence Analysis +|
|titleUrl||http://mtgroup.ict.ac.cn/~sunjian/IE/cardie aaai93.pdf +|