16822197
Generating Original Structure in Regulatory Documents
As technology and society continue to evolve, the size of the corpus of government policies and procedures continues to more than keep pace. The U.S. Federal Tax Code today consumes over 2.8 million words or 6000 pages. There are more than 20,000 cross-references both within the code itself and to external regulations. Navigating the sea of information is a daunting task for the IRS let alone a well-intentioned tax payer, or policy-maker seeking to eliminate redundancies, inconsistencies, or loopholes. While tools for tasks such as compliance checking or query answering have long held promise, automated reasoning, however intelligent, needs something to reason upon, a formalized knowledge base of some kind. In other domains people may be the primary targets of knowledge engineering; in the policy realm, much of the requisite knowledge resides in legal and regulatory documents.
45512564
Constraint-driven hierarchical information extraction
This abstract summarizes a propsal to use semistructured constraints as a general framework for information extraction. The proposal builds on the intuition that written documents tend to contain a great deal of redundancy: Tell them what you are going to tell them, tell them, tell them what you told them. While current methods overlook or eliminate redundancy, we seek to explicitly represent and exploit both repetition and other natural patterns in semistructured text. We summarize our early research on verification, outline current and future directions, and identify related work. Our initial work assumes knowledge of both document structure and its attendant constraints for the purpose of extraction verification. Specifically, semantic and structural constraints are used to check wrapper accuracy. For example, a structural constraint might ensure that the parties to a contract are introduced at the beginning of the document. A foreign-key constraint checks whether the parties are included on a list of approved signers. That signatories are repeated at the end of a contract is represented by an inverse constraint. A wrapper to extract seven attributes from legal arbitration decisions was applied to 200 cases. Constraint-based verification produced only ten false negatives, the majority of which were due to errors in wrapper delimiters. Further results were reported earlier [Hunter, et al., 2002]. We are currently experimenting with semistructured constraints in hierarchical extraction. Documents with the same semantic and structural constraints are placed in the same class. Class-specific routines fragment the document, extract from each fragment, and recurse on selected components. We propose the use of semistructured constraints as a framework for both fragmentation and extraction. For example, in addition to simple features like HTML-nesting, constraints can dictate whether certain values or structural properties functionally determine other fragments. A dissenting opinion must correspond to a legal decision with more than one judge.
1878850
Information Brokering on the World Wide Web
Information Brokering is the process of collecting and re-distributing information. As the rich but unstructured sea of data that comprises the World Wide Web continues to grow, so too will the demand for information brokering services. This paper outlines a methodology and an architecture to support information brokering on the Web. Specifically, the two innovations which are described facilitate the construction and distribution of customized views that integrate data from a number of Web sources. An original technique is presented for the extraction of data from the Web and a solution based on JavaScript and HTML is presented to support the embedding of SQL queries within Web documents.
5750510
Online Query Relaxation via Bayesian Causal Structures Discovery
We introduce a novel algorithm, TOQR, for relaxing failed queries over databases, that is, over-constrained DNF queries that return an empty result. TOQR uses a small dataset to discover the implicit relationships among the domain attributes, and then it exploits this domain knowledge to relax the failed query. TOQR starts with a relaxed query that does not include any constraint, and it tries to add to it as many as possible of the original constraints or their relaxations. The order in which the constraints are added is derived from the domain's causal structure, which is learned by applying the TAN algorithm to the small training dataset. Our experiments show that TOQR clearly outperforms other approaches: even when trained on a handful of examples, it successfully relaxes more that 97% of the failed queries; furthermore, TOQR'S relaxed queries are highly similar to the original failed query.
39601893
Smoothing parameter selection for smoothing splines: a simulation study
Smoothing splines are a popular method for performing nonparametric regression. Most important in the implementation of this method is the choice of the smoothing parameter. This article provides a simulation study of several smoothing parameter selection methods, including two so-called risk estimation methods. To the best of the author's knowledge, the empirical performances of these two risk estimation methods have never been reported in the literature. Empirical conclusions from and recommendations based on the simulation results will be provided. One noteworthy empirical observation is that the popular method, generalized cross-validation, was outperformed by another method, an improved Akaike Information criterion, that shares the same assumptions and computational complexity.
16028378
Improving the Performance of Infomax Using Statistical Signal Processing Techniques
In this paper, we present a new method that speeds up the convergence of the infomax algorithm proposed by Bell and Sejnowski. One effect of the infomax algorithm is that the 2nd order and 4th order statistical correlations are reintroduced to the signals during the learning process due to the optimization with respect to the complete signal statistics. We show that repetitively forcing 2nd and 4th order correlations to zero speeds up the convergence and improves separating sources with fewer data points.
16239260
Hybrid local polynomial wavelet shrinkage: wavelet regression with automatic boundary adjustment
An usual assumption underlying the use of wavelet shrinkage is that the regression function is assumed to be either periodic or symmetric. However, such an assumption is not always realistic. This paper proposes an effective method for correcting the boundary bias introduced by the inappropriateness of such periodic or symmetric assumption. The idea is to combine wavelet shrinkage with local polynomial regression, where the latter regression technique is known to possess excellent boundary properties. Simulation results from both the univariate and bivariate settings provide strong evidence that the proposed method is extremely effective in terms of correcting boundary bias.
767826
The Spectral Independent Components of Natural Scenes
We apply independent component analysis (ICA) for learning an efficient color image representation of natural scenes. In the spectra of single pixels, the algorithm was able to find basis functions that had a broadband spectrum similar to natural daylight, as well as basis functions that coincided with the human cone sensitivity response functions. When applied to small image patches, the algorithm found homogeneous basis functions, achromatic basis functions, and basis functions with overall chromatic variation along lines in color space. Our findings suggest that ICAm ay be used to reveal the structure of color information in natural images.