R. Jones, A. McCallum, K. Nigam, and E. Rilo#. Bootstrapping for text learning tasks. In IJCAI-99 Workshop on Text Mining: Foundations, Techniques and Applications, 1999. Home/Search Document Details and Download
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On the Automated Classification of Web Sites - Pierre (2001) (2 citations) (Correct)
....non text assets. Better acceptance of metadata is one key to the future of the semantic web. However, creation of quality metadata is tedious and is itself a prime candidate for automated methods. A preliminary method such as the one outlined in the paper can serve as the basis for bootstrapping[23] a more sophisticated classifier that takes full advantage of the semantic web, and so on. 7 Acknowledgements I would like to thank for Bill Wohler for collaboration on system design and software implementation, and Roger Avedon, Mark Butler, and Ron Daniel for useful discussions. Special thanks ....
R. Jones, A. McCallum, K. Nigam, and E. Riloff. Bootstrapping for Text Learning Tasks. In IJCAI-99 Workshop on Text Mining: Foundations, Techniques and Applications, 52-63, 1999.
On the Automated Classification of Web Sites - Pierre (2001) (2 citations) (Correct)
....non text assets. Better acceptance of metadata is one key to the future of the semantic web. However, creation of quality metadata is tedious and is itself a prime candidate for automated methods. A preliminary method such as the one outlined in the paper can serve as the basis for bootstrapping[23] a more sophisticated classifier that takes full advantage of the semantic web, and so on. 7 Acknowledgements I would like to thank for Bill Wohler for collaboration on system design and software implementation, and Roger Avedon, Mark Butler, and Ron Daniel for useful discussions. Special thanks ....
R. Jones, A. McCallum, K. Nigam, and E. Rilo#. Bootstrapping for Text Learning Tasks. In IJCAI-99 Workshop on Text Mining: Foundations, Techniques and Applications, 52-63, 1999.
Practical Issues for Automated Categorization of Web Sites - Pierre (2000) (5 citations) (Correct)
....investigated. Better acceptance of metadata is one key to the future of the semantic web. However, creation of quality metadata is tedious and is itself a prime candidate for automated methods. A preliminary method such as the one outlined in the paper can serve as the basis for bootstrapping [23] a more sophisticated classifier that takes full advantage of the semantic web, and so on. 7 Acknowledgements I would like to thank Roger Avedon, Mark Butler, and Ron Daniel for collaboration on the design of the system, and Bill Wohler for collaboration on system design and software ....
R. Jones, A. McCallum, K. Nigam, and E. Rilo#. Bootstrapping for Text Learning Tasks. In IJCAI-99 Workshop on Text Mining: Foundations, Techniques and Applications, 52-63, 1999.
Practical Issues for Automated Categorization of Web Sites - Pierre (2000) (5 citations) (Correct)
....investigated. Better acceptance of metadata is one key to the future of the semantic web. However, creation of quality metadata is tedious and is itself a prime candidate for automated methods. A preliminary method such as the one outlined in the paper can serve as the basis for bootstrapping [23] a more sophisticated classifier that takes full advantage of the semantic web, and so on. 7 Acknowledgments I would like to thank Roger Avedon, Mark Butler, and Ron Daniel for collaboration on the design of the system, and Bill Wohler for collaboration on system design and software ....
R. Jones, A. McCallum, K. Nigam, and E. Rilo#. Bootstrapping for Text Learning Tasks. In IJCAI-99 Workshop on Text Mining: Foundations, Techniques and Applications, 52-63, 1999. John M. Pierre: Practical Issues for Automated Categorization of Web Sites 7
Automatically Augmenting Terminological Lexicons from.. - George Demetriou And (Correct)
....texts could therefore be of great help to system developers. There has been recently an increased interest in techniques of bootstrapping for automatic term acquisition from untagged text in relation to Named Entity (NE) recognition tasks (Collins and Singer, 1999; Cucerzan and Yarowsky, 1999; Jones et al. 1999). A bootstrapping approach to term acquisition is based on the distributional hypothesis that entities of the same semantic class usually occur in similar contextual environments. In the management succession domain (DARPA, 1995) for example, the names of persons and locations frequently occur in ....
....new seeds for extracting new patterns and so on. Using a bootstrapping method for term identification is an attractive option because there is no need to annotate the texts with labels of name classes and, usually, examples of terms that could be used as seeds can be found easily. The approach by Jones et al. 1999) uses a bootstrapping technique for learning the names of locations in WWW pages. They first initialise a learner module with a few seed words and then run AutoSlog (an extraction system that uses heuristics in the form of domain independent linguistic rules) to generate extraction patterns and ....
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R. Jones, A. McCallum, K. Nigam, and E. Riloff. 1999. Bootstrapping for text learning tasks. In IJCAI'99 Workshop on Text Mining: Foundations, Techniques and Applications, pages 52--63, Stockholm, Sweden.
High Precision Information Extraction - Rich Caruana Center (Correct)
....uses the workbench to extract the information from the source text. This approach is similar in spirit to the interactive extraction system proposed in [3] A related approach that places more emphasis on learning to extract information from a small sample of labeled examples is discussed in [4, 5]. The approach presented in this paper does not depend on machine learning, and instead emphasizes using a human expert to make all critical decisions. While this makes the process less automatic, it also helps us obtain the very high precision needed for our problems. This paper briefly ....
R. Jones, K. Nigam, A. McCallum, and E. Riloff. Bootstrapping for text learning tasks. In IJCAI-99 Workshop on Text Mining: Foundations, Techniques and Applications, 1999.
Improving Short-Text Classification Using Unlabeled.. - Zelikovitz, Hirsh (2000) (2 citations) (Correct)
....EM algorithm iterates until there is no change in the naive Bayes parameters. Nigam et al. present a number of experimental results that show that error rates can be reduced significantly using unlabeled examples in this way. Other related algorithms are described by McCallum and Nigam (1999) and Jones et al. 1999). Blum and Mitchell s (1998) co training algorithm also uses unlabeled data to improve learning. Their algorithm applies to problems where the target concept can be described in two redundantly sufficient ways (such as through two different subsets of attributes describing each example) Each ....
Jones, R., McCallum, A., Nigam, K., & Riloff, E. (1999). Bootstrapping for text learning tasks. Working Notes of the IJCAI-99 Workshop on Text Mining: Foundations, Techniques and Applications (pp. 52--63).
Learning to Extract Entities from Labeled and Unlabeled Text - Jones (2005) Self-citation (Jones Rilo) (Correct)
No context found.
Jones, R., Nigam, K., McCallum, A., & Rilo#, E. (1999). Bootstrapping for text learning tasks. IJCAI-99 Workshop on Text Mining: Foundations, Techniques and Applications.
Text Clustering with Extended User Feedback - Huang, Mitchell (2006) (Correct)
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R. Jones, A. McCallum, K. Nigam, and E. Rilo#. Bootstrapping for text learning tasks. In IJCAI-99 Workshop on Text Mining: Foundations, Techniques and Applications, 1999.
Semi-Supervised Training of Models for Appearance-Based.. - Rosenberg (2004) (Correct)
No context found.
Rosie Jones, Andrew McCallum, Kamal Nigam, and Ellen Riloff. Bootstrapping for Text Learning Tasks. In IJCAI-99 Workshop on Text Mining: Foundations, Techniques and Applications, pp. 52-63. 1999.
Automating the Extraction of Data From Html Tables - Embley, Tao, Liddle (2003) (Correct)
No context found.
R. Jones, A. McCallum, K. Nigam, and E. Rilo#. Bootstrapping for text learning tasks. In IJCAI-99 Workshop on Text Mining: Foundations, Techniques, and Applications, pages 52--63, Stockholm, Sweden, 1999.
Models for Information Extraction - Peng (Correct)
No context found.
:Rosie Jones, Andrew McCallum, Kamal Nigam, Ellen Rilo , Bootstrapping for Text Learning Tasks,IJCAI-99
Multilingual Coreference Resolution - Harabagiu, Maiorano (Correct)
No context found.
Rosie Jones, Andrew McCallum, Kevin Nigam and Ellen Rilo . 1999. Bootstrapping for Text Learning Tasks.
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