Method for learning and combining global and local...

Data processing: artificial intelligence – Machine learning

Reexamination Certificate

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C701S001000

Reexamination Certificate

active

06892189

ABSTRACT:
A method is provided for information extraction and classification which combines aspects of local regularities formulation with global regularities formulation. A candidate subset is identified. Then tentative labels are created so they can be associated with elements in the subset that have the global regularities, and the initial tentative labels are attached onto the identified elements of the candidate subset. The attached tentative labels are employed to formulate or “learn” initial local regularities. Further tentative labels are created so they can be associated with elements in the subset that have a combination of global and local regularities, and the further tentative labels are attached onto the identified elements of the candidate subset. Each new dataset is processed with reference to an increasingly-refined set of global regularities, and the output data with their associated confidence labels can be readily evaluated as to import and relevance.

REFERENCES:
patent: 5809493 (1998-09-01), Ahamed et al.
patent: 5930803 (1999-07-01), Becker et al.
patent: 5963940 (1999-10-01), Liddy et al.
patent: 6026388 (2000-02-01), Liddy et al.
patent: 6161130 (2000-12-01), Horvitz et al.
patent: 6233575 (2001-05-01), Agrawal et al.
patent: 6304864 (2001-10-01), Liddy et al.
patent: 6389436 (2002-05-01), Chakrabarti et al.
patent: 6516308 (2003-02-01), Cohen
patent: 6571225 (2003-05-01), Oles et al.
Recognizing Structures in Web Pages Using Similarity Queries, William W. Cohen, (1999) American Association for Artificial Intelligence.*
Learning Page-Independent Heuristics for Extracting Data From Web Pages, Cohen W. W., and Fan, W. (1999) Proceeding of the 1998 American Association for Artificial Intelligence Spring Symposium on Intelligent Agents in Cyberspace.*
Fast Effective Rule Induction In Machine Learning, Cohen W. W. (1995) Proceedings of the Twelth International Conference, Lake Tahoe, California, Moran Kaufmann.*
Integration of Heterogeneous Databases Without Common Domains Using Queries Based On Textual Similarity, Cohen W. W., (1998) Proceedings of ACM SIGMOD-98.*
A Web-based Information System That Reasons With Structured Collections of Text, Cohen W. W. , (1998) Proceedings of Autonomous Agents-98.*
A Robust System Architectrure for Mining Semi-structured Data, Lisa Singh, Bin Chen, Rebecca Haight, Peter Scheuermann, Kiyoto Aoki (1998) American Association for Artificial Intelligence, NEC Research Institute ( CITESEER ).*
Generating Association Rules from Semi-Structured Documents Using an Extended Concept Hierarchy, Lisa Sign Peter Scheuermann, Bin Chen, (1997) NEC Research Institute ( CITESEER ) pp. 1-8.*
Towards Heterogeneous Multimedia Information Systems: The Garlic Approach, Carey et al., IEEE, (1995) Research Issues in Data Engineering, Distributed Object Management, Proceeding RIDE-DOM Fifth International Workshop, pps. 124-131.*
Type Classification of Semi-Structured Documents, Markus Tresch, Neal Palmer, Allen Luniewski, Proceeding of the 21stVLDB Conference, Zurich, Switzerland, (1995) pp. 263-274.*
Knowledge Discovery Using KNOW-IT (KNOWledge Base Information Tools); Elizabeth D. Liddy (1998) IEEE, pps. 67-70.*
FeaFeature Selection in Text Categorization Usin the Baldwin Effect; Edmund S. Yu & Elizabeth D. Liddy; (1999), IEEE, pps. 2924-2927.*
NLP-Supported Decision-Making, Elizabeth D. Liddy, (1999) IEEE, pps. 390-391.*
A Classification of Multi-Database Languages, Markus Tresch; Mark H. Scholl; (1994) IEEE, Parallel and DEstributed Information Systems, Proceeding of the Third International Conference, pps. 195-202.*
Agichitien et al. “Snowball: Extracting Relations from Large Plain-Text Collections,” Proceedings of the fifth ACM conference on Digital libraries, pp. 85-94 (2000).
Brin “Extracting Patterns and Relations from the World Wide Web,” The Proceedings of the 1998 International Workshop on the Web and Databases, pp. 172-183 (1998).
Chen et al. “Data Mining: An Overview from a Database Perspective,” IEEE Transactions on Knowledge and Data Engineering 8:866-883 (1996).
Cohen “Recognizing Structure in Web Pages using Similarity Queries,” Proceedings of the sixteenth national conference on Artificial Intelligence and eleventh innovation applications of AI conference on artificial intelligence and innovative applications of artificial intelligence, pp. 59-66 (1999).
Collins et al. “Unsupervised Models for Named Entity Classification,” Proceedings of the Joint SIGDAT Conference on Empirical Methods in Natural Language Processing and Very Large Corpora, pp. 169-196 (1999).
Cooley et al. “Grouping Web Page References into Transactions for Mining World Wide Web Browsing Patterns,” Proceedings of the 1997 IEEE Knowledge and Data Engineering Exchange Workshop (KDEX '97), pp. 2-9 (1997).
Garofalakis et al “Data Mining and the Web: Past, Present and Future,” Proceedings of the ACM CIKM'99 2nd Workshop on Web Information and Data Management (WIDM'99), pp 43-47 (1999).
Kawano “Mondou: Web search engine with textual data mining,” Proceedings of the IEEE Pacific Rim Conference of Communications, Computers and Signal Processing, pp. 402-405 (1997).
Lin et al. “Extracting Classification Knowledge of Internet Documents with Mining Term Associations: A Semantic Approach,” Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval, pp. 241-249 (1998).
Nigam et al. “Learning to Classify Text from Labeled and Unlabeled Documents,” The Proceedings of the Fifteenth National Conference on Artificial Intelligence, pp. 792-799 (1998).
Riloff et al. “Learning Dictionaries for Information Extraction by Multi-level Bootstrapping,” Proceedings of the sixteenth national conference on artificial intelligence and eleventh innovation applications of AI conference on Artificial intelligence and innovative applications of artificial Intelligence, pp. 474-479 (1999).

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