Method for learning to infer the topical content of documents ba

Details Method for learning to infer the topical content of documents ba Method for learning to infer the topical content of documents ba

1994-09-16

1997-11-11

Black, Thomas G.

395606, G06F 1730

Patent

active

056873646

ABSTRACT:
An unsupervised method of learning the relationships between words and unspecified topics in documents using a computer is described. The computer represents the relationships between words and unspecified topics via word clusters and association strength values, which can be used later during topical characterization of documents. The computer learns the relationships between words and unspecified topics in an iterative fashion from a set of learning documents. The computer preprocesses the training documents by generating an observed feature vector for each document of the set of training documents and by setting association strengths to initial values. The computer then determines how well the current association strength values predict the topical content of all of the learning documents by generating a cost for each document and summing the individual costs together to generate a total cost. If the total cost is excessive, the association strength values are modified and the total cost recalculated. The computer continues calculating total cost and modifying association strength values until a set of association strength values are discovered that adequately predict the topical content of the entire set of learning documents.

REFERENCES:
patent: 4247892 (1981-01-01), Lawrence
patent: 4661924 (1987-04-01), Okamoto et al.
patent: 4775956 (1988-10-01), Kaji et al.
patent: 4823306 (1989-04-01), Barbic et al.
patent: 4839853 (1989-06-01), Deerwester et al.
patent: 4849898 (1989-07-01), Adi
patent: 4864502 (1989-09-01), Kucera et al.
patent: 5020019 (1991-05-01), Ogawa
patent: 5113340 (1992-05-01), McWherter
patent: 5265065 (1993-11-01), Turtle
patent: 5301109 (1994-04-01), Landauer et al.
patent: 5317507 (1994-05-01), Gallant
patent: 5325298 (1994-06-01), Gallant
patent: 5418951 (1995-05-01), Damashek
patent: 5442778 (1995-08-01), Pedersen et al.
patent: 5479576 (1995-12-01), Watnabe et al.
Salton et al, A Vector Space Model for Automatic Indexing, Dialogue, pp. 613-620, Nov. 1975.
Kimoto et al, Automatic Indexing System for Japanese Text, Dialogue, pp. 51-56, Dec. 1989.
Salton G, A Theory of Indexing, Dialogue, pp. 1-56, Jul. 1975.
Kimoto et al, A Dynamic Thesaurus and its Application to Associated Information Retrieval, IEEE, pp. 19-29, 1991.
Yasunobu et al, Document Retrieval Expert System Shell with Worksheet-Based Knowledge Acquisition Facility, IEEE, pp. 278-285, 1989.
Deerwester, S., Dumais, S.T., Furnas, G.W., Landauer, T.K., Harshman, R. Indexing by Latent Semantic Analysis. Journal of the American Society for Information Science. Sep. 1990; pp. 391-407.
DeJong, G.F., Skimming Newspaper Stories by Computer. Research Report #104, May 1977; pp. 1-31.
Dumais, S.T., Nielsen, J.Automating the Assignment of Submitted Manuscripts to Reviewers. Belkin, Ingwersen, and Pejtersen, Editors. Proceedings of the Fifteenth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval; Jun. 1992; Copenhagen, Denmark. pp. 233-244.
Hearst, Marti A., Plaunt, Christian. Subtopic Structuring for Full-Length Document Access. Korfhage, Rasmussen, Willett, Editors. Proceedings of the Sixteenth Annual International ACm SIGIR Conference on Research and Development in Information Retrieval; Jun. 27-Jul. 1, 1993; Pittsburgh, PA; pp. 59-68.
Jacobs, P.S., Rau, L.F. SCISOR: Extracting Information from On-line News. Sibley, E. H., Editor, Communications of the ACM, Nov. 1990, vol. 33, No. 11; pp. 88-97.
Masand, B., Linoff, G., Waltz, D. Classifying News Stories using Memory Based Reasoning. Belkin, Ingwersen, and Pejtersen, Editors. Proceedings of the Fifteenth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval; Jun. 1992; Copenhagen, Denmark. pp. 59-65.
McCune, P., Tong, R.M., Dean, J.S., Shapiro, D.G. RUBRIC: A System for Rule-Based Information Retrieval. IEEE Transactions on Software Engineering, vol. SE-11, No. 9, Sep. 1985; pp. 939-945.
Parabolic Interpolation and Brent's Method in One-Dimension. Numerical Recipes in C, The Art of Scientific Computing. Cambridge University Press; pp. 299-302, 315-322, no date.
Phillip J. Hayes. Intelligent high-volume text processing using shallow, domain-specific techniques. In Paul S. Jacobs, editor, Text-Based Intelligent Systems: Current Research and Practice in Information Extraction and Retrieval, pp. 227-242. Lawrence Erlbaum Associates, 1992.
Riloff, E., Lehnert, W. Classifying Texts Using Relevancy Signatures. Natural Language: Parsing; pp. 329-334, no date.
Saund, Eric. Unsupervised Learning of Mixtures of Multiple Causes in Binary Data. Spatz, Bruce M., ed., Neural Information Processing Systems 6. San Francisco, CA: Morgan Kaufmann Publishers, Inc.; 1994; pp. 27-34; 1365-1383.
Stanfill, C., Waltz, D. Toward Memory-Based Reasoning. Communications of the ACM, Dec. 1986, vol. 29, No. 12; pp. 1213-1228.
Willett, P. Recent Trends in Hierarchic Document Clustering: A Critical Review. Information Processing & Management, vol. 24, No. 5, 1988; pp. 577-597.

Affiliated with

Also associated with

Rating

Method for learning to infer the topical content of documents ba does not yet have a rating. At this time, there are no reviews or comments for this patent.

If you have personal experience with Method for learning to infer the topical content of documents ba, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Method for learning to infer the topical content of documents ba will most certainly appreciate the feedback.

Rate now

Comments { 0 }

Profile ID: LFUS-PAI-O-1236861