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Hits 1 – 20 of 21

1	Automatic question answering using the Web: beyond the factoid
	Soricut, Radu; Brill, Eric
	In: Information Retrieval Journal. - Dordrecht [u.a.] : Springer Science + Business Media B.V. 9 (2006) 2, 191-206
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2	Man [and Woman] vs. Machine: A Case Study in Base Noun Phrase Learning ...
	Brill, Eric; Ngai, Grace. - : arXiv, 2001
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3	Finding consensus in speech recognition: word error minimization and other applications of confusion networks
	Mangu, Lidia; Brill, Eric; Stolcke, Andreas
	In: Computer speech and language. - Amsterdam [u.a.] : Elsevier 14 (2000) 4, 373-400
	OLC Linguistik
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4	Finding consensus in speech recognition : word error minimization and other applications of confusion networks
	Mangu, Lidia; Brill, Eric; Stolcke, Andreas
	In: Computer speech and language. - Amsterdam [u.a.] : Elsevier 14 (2000) 4, 373-400
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5	A closer look at the automatic induction of linguistic knowledge
	Brill, Eric
	In: Learning language in logic. - Berlin [u.a.] : Springer (2000), 49-56
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6	Part-of-speech tagging
	Brill, Eric
	In: Handbook of natural language processing. - New York [u.a.] : Dekker (2000), 403-414
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7	Syntactic wordclass tagging
	Karttunen, Lauri (Mitarb.); Leech, Geoffrey N. (Mitarb.); Daelemans, Walter (Mitarb.). - Dordrecht [u.a.] : Kluwer, 1999
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	UB Frankfurt Linguistik
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8	Natural language processing using very large corpora
	Wu, Dekai (Mitarb.); Church, Kenneth W. (Hrsg.); Radev, Dragomir R. (Mitarb.). - Dordrecht [u.a.] : Kluwer, 1999
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	UB Frankfurt Linguistik
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9	An information-theoretic solution to parameter setting
	Brill, Eric; Kapur, Shyam
	In: Cornell working papers in linguistics. - Ithaca, NY : Dep. 15 (1997), 200-216
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10	Recent advances in parsing technology
	Raaijmakers, Stephan (Mitarb.); Rönnquist, Ralph (Mitarb.); Strzalkowski, Tomek (Mitarb.). - Dordrecht [u.a.] : Kluwer, 1996
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	UB Frankfurt Linguistik
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11	Transformation-Based Error-Driven Learning and Natural Language Processing: A Case Study in Part-of-Speech Tagging
	Brill, Eric
	In: Computational linguistics. - Cambridge, Mass. : MIT Press 21 (1995) 4, 543-566
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12	Transformation-based error-driven learning and natural language processing : a case study in part-of-speech tagging
	Brill, Eric
	In: Computational linguistics. - Cambridge, Mass. : MIT Press 21 (1995) 4, 543-565
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13	Some Advances in Transformation-Based Part of Speech Tagging ...
	Brill, Eric. - : arXiv, 1994
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14	A Report of Recent Progress in Transformation-Based Error-Driven Learning
	Brill, Eric
	In: DTIC (1994)
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15	PEGASUS: A Spoken Language Interface for On-Line Air Travel Planning
	Zue, Victor; Seneff, Stephanie; Polifroni, Joseph...
	In: DTIC (1994)
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16	Automatic Grammar Induction and Parsing Free Text: A Transformation-Based Approach
	Brill, Eric
	In: DTIC (1993)
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17	A Corpus-Based Approach to Language Learning
	Brill, Eric D
	In: IRCS Technical Reports Series (1993)
	Abstract: One goal of computational linguistics is to discover a method for assigning a rich structural annotation to sentences that are presented as simple linear strings of words; meaning can be much more readily extracted from a structurally annotated sentence than from a sentence with no structural information. Also, structure allows for a more in-depth check of the well-formedness of a sentence. There are two phases to assigning these structural annotations: first, a knowledge base is created and second, an algorithm is used to generate a structural annotation for a sentence based upon the facts provided in the knowledge base. Until recently, most knowledge bases were created manually by language experts. These knowledge bases are expensive to create and have not been used effectively in structurally parsing sentences from other than highly restricted domains. The goal of this dissertation is to make significant progress toward designing automata that are able to learn some structural aspects of human language with little human guidance. In particular, we describe a learning algorithm that takes a small structurally annotated corpus of text and a larger unannotated corpus as input, and automatically learns how to assign accurate structural descriptions to sentences not in the training corpus. The main tool we use to automatically discover structural information about language from corpora is transformation-based error-driven learning. The distribution of errors produced by an imperfect annotator is examined to learn an ordered list of transformations that can be applied to provide an accurate structural annotation. We demonstrate the application of this learning algorithm to part of speech tagging and parsing. Successfully applying this technique to create systems that learn could lead to robust, trainable and accurate natural language processing systems.
	URL: https://repository.upenn.edu/cgi/viewcontent.cgi?article=1193&context=ircs_reports https://repository.upenn.edu/ircs_reports/191
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18	A corpus-based approach to language learning
	Brill, Eric David
	In: Dissertations available from ProQuest (1993)
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19	Automatically Acquiring Phrase Structure Using Distributional Analysis
	Brill, Eric; Marcus, Mitchell
	In: DTIC (1992)
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20	A Simple Rule-Based Part of Speech Tagger
	Brill, Eric
	In: DTIC (1992)
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