Ronen Feldman, Yair Liberzon, Binyamin Rosenfeld, Jonathan Schler  Home/Search
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Abstract: Information extraction is one of the most important techniques used in Text Mining. One of the main problems in building information extraction (IE) systems is that the knowledge elicited from domain experts tends to be only approximately correct. In addition, the knowledge acquisition phase for building IE rules usually takes a tremendous amount of time on the part of the expert and of the linguist creating the rules. We therefore need an effective means of revising our IE rules whenever we... (Update)
Context of citations to this paper: More
...a description of the revision algorithm used in the FRST system. This algorithm is based on the PTR algorithm [Feldman 1993, Feldman et al. 1994] for revision of logical domain theories. not(working state engine ) not(repair ) determine engine state (working state engine...
...has been done in the past. Apart from search engines, work related to ours includes Web information extraction (e.g. wrappers [3] 10][14][16] Web queries languages [8] 26] user preferences [32] etc) definition finding [19] and questionanswering [11] 17] 18] 22] in...
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BibTeX entry: (Update)
Feldman R. and Nedellec C. "A Framework for Specifying Explicit Bias for Revision of Approximate Knowledge Bases," 7th International Workshop on Knowledge Acquisition, Banff, Canada, Feb. 1994. http://citeseer.ist.psu.edu/feldman94framework.html More
@inproceedings{ feldman00framework,
author = "Ronen Feldman and Yair Liberzon and Binyamin Rosenfeld and Jonathan Schler and Jonathan Stoppi",
title = "A framework for specifying explicit bias for revision of approximate information extraction rules",
booktitle = "Knowledge Discovery and Data Mining",
pages = "189-197",
year = "2000",
url = "citeseer.ist.psu.edu/feldman94framework.html" }
Citations (may not include all citations):1359 Induction on Decision trees (context) - Quinlan - 1986
149 Learning to Extract Symbolic Knowledge from the World Wide W.. - Craven, DiPasquo et al.
117 The Utility of Knowledge in Inductive Learning - Pazzani, Kibler - 1990
105 Learning Information Extraction Rules for Semi-structured an.. - Soderland - 1999
89 Changing the rules: A comprehensive approach to theory revis.. (context) - Ourston, Mooney - 1990
70 Relational learning of pattern-match rules for information - Califf, Mooney - 1997
50 Information Extraction as a Basis for High-Precision Text Cl.. - Riloff, Lehnert
39 First-Order Theory Revision (context) - Richards, Mooney - 1991
37 Refinement of approximately correct domain theories by knowl.. (context) - Towell, Shavlik et al. - 1990
33 Learning information extraction patterns from examples - Huffman - 1996
26 Multistrategy learning for information extraction - Freitag - 1998
17 Revising Relational Domain Theories (context) - Wogulis - 1991
15 Combining Neural and Symbolic Learning to Revise Probabilist.. (context) - Mahoney, Mooney - 1993
13 Text Mining at the Term Level - Feldman, Fresko et al. - 1998
13 Detecting and Correcting Errors of Omission after Explanatio.. (context) - Pazzani - 1989
8 Mining Text Using Keyword Distributions (context) - Feldman, Dagan et al. - 1998
6 Visualization Techniques to Explore Data Mining Results for .. (context) - Feldman, Klsgen et al. - 1997
6 Incremental Refinement of Approximate Domain Theories (context) - Feldman, Segre et al. - 1991
5 Text Mining via Information Extraction (context) - Feldman, Aumann et al. - 1999
4 Description of the UMass Systems as Used for MUC-6 (context) - Fisher, Soderland et al. - 1995
3 Circle Graphs: New 196 Visualization Tools for Text-Mining (context) - Aumann, Feldman et al. - 1999
1 and Tyson M (context) - Appelt, Hobbs et al. - 1993
1 Trend Graphs: Visualizing the Evolution of Concept Relations.. (context) - Feldman, Aumann et al. - 1998
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