Context vector generation and retrieval

Details Context vector generation and retrieval Context vector generation and retrieval

2007-07-31

2007-07-31

Hirl, Joseph P (Department: 2129)

Data processing: artificial intelligence

Neural network

C706S020000, C706S026000

Reexamination Certificate

active

09672237

ABSTRACT:
A system and method for generating context vectors for use in storage and retrieval of documents and other information items. Context vectors represent conceptual relationships among information items by quantitative means. A neural network operates on a training corpus of records to develop relationship-based context vectors based on word proximity and co-importance using a technique of “windowed co-occurrence”. Relationships among context vectors are deterministic, so that a context vector set has one logical solution, although it may have a plurality of physical solutions. No human knowledge, thesaurus, synonym list, knowledge base, or conceptual hierarchy, is required. Summary vectors of records may be clustered to reduce searching time, by forming a tree of clustered nodes. Once the context vectors are determined, records may be retrieved using a query interface that allows a user to specify content terms, Boolean terms, and/or document feedback. The present invention further facilitates visualization of textual information by translating context vectors into visual and graphical representations. Thus, a user can explore visual representations of meaning, and can apply human visual pattern recognition skills to document searches.

REFERENCES:
patent: 4959870 (1990-09-01), Tachikawa
patent: 5005206 (1991-04-01), Naillon et al.
patent: 5161204 (1992-11-01), Hutcheson et al.
patent: 5239594 (1993-08-01), Yoda
patent: 5263097 (1993-11-01), Katz et al.
patent: 5274714 (1993-12-01), Hutcheson et al.
patent: 5287275 (1994-02-01), Kimura
patent: 5313534 (1994-05-01), Burel
patent: 5317507 (1994-05-01), Gallant
patent: 5325298 (1994-06-01), Gallant
patent: 5465308 (1995-11-01), Hutcheson et al.
Brown, P.F., et al. “A Statistical approach to Machine Translation”, Computational Linguistics (Jun. 1990) vol. 16. No. 2. p. 79-85.
Crouch, C.J., “An Approach to the Automatic Construction of Global Thesauri,” Information Processing& Management, (1990), vol. 26, No. 5, pp. 629-640.
Cutting, D.R., “ScatterlGather: A Cluster-based Approach to Browsing Large Document Collections,” 1 5th Ann Int'l SIGIR, (1 992), pp. 1-12.
Deerwester, et al., “Indexing by Latent Semantic Analysis,” Journal of the American Society for Information Science, (1 990) 41 (6) pp. 391-407.
Dowe, J., “Content-based Retrieval in Multimedia Imaging,” Proc. SPIE, vol. 1908, Apr. 1993, pp. 164-167.
Egghe, L., “A New Method for Information Retrieval, Based on the Theory of Relative Concentration,”Proceedings of the 13′—International Conference on Research and Development in Information Retrieval, (Sep. 5-7, 1990), pp. 469-493.
Evans, et al., “Automatic Indexing Using Selective NLP And First-Order Thesauri,” Department of Philosophy and Computer Science Laboratory for Computational Linguistics, Carnegie Mellon University, Pittsburgh, PA, pp. 624-639.
Gallant, S.I., “A Practical Approach for Representing Context and for Performing Word Sense Disambiguation Using Neural Networks,” Neural Comutation 3, (1991), pp. 293-309.
Grefenstett, G., “Use of Syntactic Context to Produce Term Association Lists for Text Retrieval,”Computer Science Department, University of Pittsburgh, Pittsburgh, PA, (1992), pp. 89-97.
Kimoto, H., et al., “Construction of a Dynamic Thesaurus and Its Use for Associated InformationRetrieval,” Proceedings of the 13th International Conference on Research and Development in Information Retrieval, (Sep. 5-7, 1990), pp. 227-241.
Kwok, K.L., “A Neural Network for Probabillistic Information Retrieval,” Proceedings of the Twelfth Annual international ACMSIGIR Conference on Research and Development in Information Retrieval, (Jun. 25-28, 1980), pp. 21-30.
Liddy, et al., “Statistically-Guided Word Sense Disambiguation,” School of Information Studies, Syracuse University, Syracuse, New York, pp. 98-107.
Lin, X., et al., “A Self-organizing Semantic Map for information Retrieval”, Proceedings of the International ACM/SIGIR Conference on Research and Development in Information Retrieval, (1991), pp. 262-269.
McCune , et al., “Rubric: A System for Rule-Based Information Retrieval.” IEEE Transactions on Software Engineering, (1985), vol. SE-11, No. 9, pp. 939-945.
Myamoto, et al., “Generation of a Pseudothesaurus for information Retrieval Based on Co-occurrences and Fuzzy Set Operations.” IEEE Transactions on Systems, Man, Cybernetics, (Jan./Feb. 1983), vol. SMC-13, No. 1 ., p. 62-70.
Niblack, W., “QBIC Project: Querying Images by Content, Using Color, Texture, and Shape,” Proc. (SPIE, vol. 1908, Apr. 1993, pp. 173-187.
Peat, et al., “The Limitations of Term Co-Occurrence Data for Query Expansion in Document Retrieval Systems,” Journal of the American Society for information science-(1991), 42(5), pp. 378-383.
Qiu, et al, “Concept Based Query Expansion,” Department of Comuter Science. Swiss Federal Institute of Technology, Zurich, Switzerland, pp. 160-169.
Ruge, G., “Experiments on Linguistically-Based Term Associations,” information Processing & Management, (1992), vol. 28, No. 3, pp. 317-332.
Salton, G., et al. “A Vector Space model for Automatic Indexing”, Comm. Of the ACS, (Nv. 1975) vol. 18, No. 11, pp. 613-620.
Salton, G., et al., “Introduction to Modern Information Retrieval,” McGraw-Hill Book Company, 118-155.
Sekine, S., et al., “Automatic Learning for Semantic Collocation.”
Schutze, H., “Dimensions of Meaning,” Proceedings Supercomputing, (Nov. 16-20, 1992), pp. 787-796.
Sutcliffe, R.F.E., “Distributed Representations in a Text Based Information Retrieval System: a New Way of Using the Vector Space Model”, Proc. Of the ACM/SIGIR Conf., Chicago, IL, (Oct. 13-16, 1 WI). pp. 123-132.
Turtle, H., et al., “Inference Networks for Document Retrieval”, Proceedings of the 13th International -Conference on Research and Development in Information Retrieval, (Sep. 5-7, 1990), pp. 1-25.
Van Rijsbergen, C.J., “A Theoretical Basis For the Use of Co-Occurrence Data in Informational Retrieval”, J of Documentation, (Jun. 1977), vol. 33, No. 2, pp. 106-119.
Voorhees, E.M., et al., “Vector Expansion in a Large Collection,” Siemens Corporate Research, Inc.,Princeton, New Jersey.
Wilks, Y., et al., “Providing Machine Tractable Dictionary Tools,” Computer Research Laboratory, 13553-05382 New Mexico State University, Las Cruces, New Mexico, pp. 98-154.
Wong, S.K.M., et al., “On Modeling of Information Retrieval Concepts in Vector Spaces,” ACM Transactions on Database Systems, (Jun. 1987). vol. 12, No. 2, pp. 299-321.
Salton, G., et al., “A Vector Space Model for Automatic Indexing,” Comm. Of the ACM, vol. 18, Nov. 1975.
Wong, S.K.M., et al., “On Modeling of Information Retrieval Concepts in Vector Spaces,” ACM Trans or Database Systems, vol. 12, pp. 299-321, 1987.
Antonini, M, et al., “Image Coding Using Wavelet Transform,” Image Processing, IEEE Transactions, Apr. 1992, vol. 1, Issue 2, CNRS, Univ. de Nice-Sophia Antipolis, Valbonne, France. 18 pages.
Petilli, Stephen G., Image Compression With Full Wavelet Transform (FWT) and Vector Quantization, Jet Propulsion Laboratory, California Institute of Technology, Pasadena, California, IEEE 1993, pp. 906-910.
Martens, R.L.J., et al., “Coding of Image Textures Using Wavelet Decomposition and Hierarchal Multirate Vector Quantization,” Dept. of Electronic Engineering, Toronto University, Ontario Canada, appears in Time-Frequency and Time-Scale Analysis, 1992, Proceedings of the IEEE-SP International Symposium, pp. 101-104.
A. Yarnamoto, et al., “Extraction of Object Features and Its Application to image Retrieval,” Trans. Of IEICE, vol. E72, No. 6, pp. 771-781, Jun. 1989.
S.K. Chang, et al.,

Affiliated with

Also associated with

Rating

Context vector generation and retrieval does not yet have a rating. At this time, there are no reviews or comments for this patent.

If you have personal experience with Context vector generation and retrieval, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Context vector generation and retrieval will most certainly appreciate the feedback.

Rate now

Comments { 0 }

Profile ID: LFUS-PAI-O-3726188