Landauer, T. K., Laham, D., & Foltz, P. W., (1998). Learning human-like knowledge by Singular Value Decomposition: A progress report. In M. I. Jordan, M. J. Kearns & S. A. Solla (Eds.), Advances in Neural Information Processing Systems 10,(pp. 4551) . Cambridge: MIT Press.  Home/Search   Document Details and Download   Summary   Related Articles   Check

....are considerably impeded during the analysis of the results of the achievements that are registered in the traditional methods of control. A major step towards the overcoming of this problem is the use of latent semantic analysis of the survey in the process of study and knowledge building [2,3]. The role of the scheme of the cognitive structures in the processes of information perception and processing as well as decision making have been derived from the ideas of the cognitive paradigm in the theory of study. That is why in the appraisal of the effects of study the built notional ....

Landauer, T. K., Laham, D., & Foltz, P. W., (1998). Learning human-like knowledge by Singular Value Decomposition: A progress report. In M. I. Jordan, M. J. Kearns & S. A. Solla (Eds.), Advances in Neural Information Processing Systems 10,(pp. 4551) . Cambridge: MIT Press.

....index, link, and navigate captured material. The NSF grant proposal Automated Understanding of Captured Experience to the Experimental Software Systems Program by the PIs is meant to examine this problem. One approach we are using is to apply statistical measures for natural language understanding [58, 7, 12, 25, 32, 49, 15, 33, 11, 59]. Statistical approaches are usually based on matching word frequencies (vocabularies) in written or spoken text. The key insight in many statistical approaches to language is that concepts can be represented by vocabularies. Using vocabularies enables concepts to emerge from the data, and ....

T. K. Landauer, D. Laham, and P. Foltz. Learning human-like knowledge by singular value decomposition: A progress report. In D. S. Touretzky, M. C. Mozer, and M. E. Hasselmo, editors, Advances in Neural Information Processing Systems 10. MIT Press, Cambridge, MA, 1998. NIPS97 proceedings, in press.

....word combinations. In Figure 10 a 1 gram histogram is shown and will be referenced to as the term document matrix. The term document matrix can contain features extracted from the documents and can be used as a signal matrix X for PCA and ICA. Recently PCA and ICA has been apply to text analysis [21, 23, 25] and in the following we shall apply both PCA and ICA on the 1 gram histogram using a the MED data set [11] The MED data set is a commonly studied collection of medical abstracts. It consists of 1033 abstracts of which 30 labels has been assigned to 696 of the documents. The goal is not to ....

T.K. Landauer, D. Laham & P. Foltz: "Learning Human-like Knowledge by Singular Value Decomposition: A Progress Report," Advances in Neural Information Processing Systems 10, MIT Press: Cambridge MA, pp. 45--51, 1998.

....retrieval results [16] Some newer methods [17] take into account the word order, although others argue that the 28 majority of the information about the meaning of a passage can be carried by words independently of their order [18] 3. 2 LATENT SEMANTIC INDEXING Latent Semantic Indexing (LSI) [19], or Latent Semantic Analysis (LSA) is a variant of earlier information retrieval techniques such as SMART. It differs from those methods in that it also extracts information about the natural co occurrences of words. It does not use any information about transition probabilities from word to ....

....LSI creates this transform matrix, or Latent Semantic Index, by performing an SVD on the matrix A and then reducing the dimensionality by keeping only the vectors associated with the most significant singular values. This analysis induces human like relationships among passages and words [19]. Comparisons to conventional methods have shown LSI perfomance ranging from comparable to 30 better [20] I use the collection of recent Newscalls as the domain specific knowledge used to create the index. Section 3.5 describes the exact process in more detail. 3.3 STEMMING When analyzing ....

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T. K. Landauer, D. Laham, and P. W. Foltz, "Learning human-like knowledge by singular value decomposition: a progress report," In Advances in Neural Information Processing Systems 10, M. I. Jordan, M. J. Kearns, and S. A. Solla, Eds., Cambridge: MIT Press, 1998, pp. 45-51.

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Thomas K. Landauer, Darrell Lahman, and Peter Foltz. Learning human-like knowledge by singular value decomposition: A progress report.

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T.K. Landauer, D. Laham, and P. Foltz. Learning human-like knowledge by singular value decomposition: A progress report. In M. Jordan, M. Kearns, and S. Solla, editors, Advances in Information Processing 10, Cambridge,Mass., 1998. The MIT Press.

No context found.

Landauer, T. K., Laham, D., and Foltz, P. (1998). Learning human-like knowledge by singular value decomposition: A progress report. In Jordan, M. I., Kearns, M. J., and sara A. Solla, editors, Advances in Neural Processing Information Systems 10, pages 45--51. The MIT Press, Cambridge Massachusetts.

No context found.

T.K. Landauer, D. Laham & P. Foltz: "Learning Human-like Knowledge by Singular Value Decomposition: A Progress Report," Adv. in NIPS 10, MIT Press, pp. 45--51, 1998.

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