44753947
Constraint-based wrapper specification and verification for cooperative information systems
In this paper, we propose the use of semistructured constraints in wrappers to mitigate the impact of poor extraction accuracy on Cooperative Information System (CIS) data quality. Wrappers are a critical element of CISs whenever the constituent information systems publish semistructured text such as forms, reports, and memos rather than structured databases. The accuracy of CIS data that stem from text depends upon the wrappers as well as the accuracy of the underlying sources. Wrapper specification is the process of defining patterns (i.e. regular expressions) to extract information from semistructured text. Wrapper verification is the process of ensuring extraction accuracy--that the extracted information faithfully reflects the underlying source. We focus on the problem of extraction accuracy. We use constraints on semistructured data for both wrapper specification and verification. Consequently, we perform extraction and verification simultaneously. We apply the concept to wrappers for a Uniform Domain Name Dispute Resolution Policy (UDRP) CIS of arbitration decisions. UDRP decisions are currently distributed across arbitration authorities on three continents. The accuracy of data extracted using constraint-based specification and verification is measured by Type I and Type II errors.
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.
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.
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.
5832781
Adaptive contrast gain control and information maximization
Contrast gain control is an important mechanism underlying the visual system's adaptation to contrast of luminance in varying visual environments. Our previous work showed that the threshold and saturation determine the preferred contrast sensitivity as well as the maximum information coding capacity of the neuronal model. In this report, we investigated the design principles underlying adaptation behavior in contrast gain control by an adaptive linear-nonlinear model. We found that an adaptive rescaling mechanism predicted by information transmission maximization can explain a variety of observed contrast gain control phenomena in neurophysiological experiments, including the divisive input-output relations, and the inverse power-law relation between response gain and input contrast. Our results suggest that contrast gain control in visual systems might be designed for information maximization.
866852
Unsupervised Classification with Non-Gaussian Mixture Models Using ICA
We present an unsupervised classification algorithm based on an ICA mixture model. The ICA mixture model assumes that the observed data can be categorized into several mutually exclusive data classes in which the components in each class are generated by a linear mixture of independent sources. The algorithm finds the independent sources, the mixing matrix for each class and also computes the class membership probability for each data point. This approach extends the Gaussian mixture model so that the classes can have non-Gaussian structure. We demonstrate that this method can learn efficient codes to represent images of natural scenes and text. The learned classes of basis functions yield a better approximation of the underlying distributions of the data, and thus can provide greater coding efficiency. We believe that this method is well suited to modeling structure in high-dimensional data and has many potential applications.
35157
A cooperative coevolutionary algorithm for multiobjective optimization
This paper presents a kind of cooperative coevolutionary algorithm (CCEA) for multi-objective optimization (MOO). In this algorithm, solutions evolve in the form of cooperative subpopulations. An archive stores non-dominated solutions and helps to evaluate individuals in the subpopulations. The mechanism of niching is applied to maintain the diversity of solutions in the archive. Meanwhile, an extending operator is designed to mine information on solution distribution from the archive and guide the search to regions that are not explored enough. Extensive simulations are performed on different benchmark problems for various multi-objective evolutionary algorithms (MOEAs) and indicate that CCEA is strongly competitive with five recent well-known MOEAs in finding a good non-dominated solution set.
17821880
Indentation and Protrusion Detection and Its Applications
In this paper, we investigated the mechanism of dividing a 2D-object border into a set of local and global indentation and protrusion segments by extending the classic curvature scale-space filtering method. The resultant segments, arranged in hierarchical structures, can represent the object shape. Applying this technique, we derived a border irregularity measure for pigmented skin lesions. The measure correlated well with experienced dermatologists' evaluations and may be useful for measuring the malignancy of the lesion. Furthermore, we can use the method to discover all the bays in an aerial map.
9084974
Isolated word recognition using modular recurrent neural networks
Abstract This paper describes a novel method of using recurrent neural networks (RNN) for isolated word recognition. Each word in the target vocabulary is modeled by a fully connected recurrent network. To recognize an input utterance, the best matching word is determined based on its temporal output response. The system is trained in two stages. First, the RNN speech models (RSM) are trained independently to capture the essential static and temporal characteristics of individual words. This is performed by using an iterative re-segmentation training algorithm which gives the optimal phonetic segmentation automatically for each training utterance. The second-stage involves mutually discriminative training among the RSMs, aiming at minimizing the probability of misclassification. A series of simulation experiments have been performed to demonstrate the effectiveness of the proposed recognition method. For the recognition of (A) 20 English words, (B) 11 Cantonese digits and (C) 58 Cantonese CV syllables, the top-1 accuracy are 91.9, 93.6 and 87.1%, respectively.