43417272
A platform for Okapi-based contextual information retrieval
We present an extensible java-based platform for contextual retrieval based on the probabilistic information retrieval model. Modules for dual indexes, relevance feedback with blind or machine learning approaches and query expansion with context are integrated into the Okapi system to deal with the contextual information. This platform allows easy extension to include other types of contextual information.
2495453
A dual index model for contextual information retrieval
In this paper, we propose a dual index model for contextual IR. For each query, we search against both document level and passage level indexes, and use the corresponding merge function to update the weights for both documents and paragraphs by combining the results from both indexes according to the granularity information in metadata. Experiments on 2004 TREC data show that a significant improvement can be made by using the dual index model.
16574049
Feature Selection with Rough Sets for Web Page Classification
Web page classification is the problem of assigning predefined categories to web pages. A challenge in web page classification is how to deal with the high dimensionality of the feature space. We present a feature reduction method based on the rough set theory and investigate the effectiveness of the rough set feature selection method on web page classification. Our experiments indicate that rough set feature selection can improve the predictive performance when the original feature set for representing web pages is large.
1605324
Using contextual information to improve retrieval performance
In this paper, we propose a contextual retrieval framework which incorporates the user and global contextual information into the probabilistic retrieval model. We investigate different techniques of using contextual information to improve information retrieval performance in details. In particular, (1) we use the related text contextual information for query expansion; (2) we use the granularity information to construct the document level index and paragraph level index; (3) we use the geographic information for filtering. In addition, a new term weighting function BM5O is proposed based on the global context information. This framework is adaptable and extensible. If there is a new context category, we can extend the existing search system to accommodate it. Finally, we report our experimental findings on TREC data sets.
25290683
Maximum genus, connectivity and minimal degree of graphs
This paper is devoted to the lower bounds on the maximum genus of graphs. A simple statement of our results in this paper can be expressed in the following form: Let G be a k-edge-connected graph with minimum degree @d, for each positive integer k(= =f(@d)@b(G), and furthermore that lim"@d"->"~f(@d)=1/2, where @b(G)=|E(G)|-|V(G)|+1 is the cycle rank of G. The result shows that lower bounds of the maximum genus of graphs with any given connectivity become larger and larger as their minimum degree increases, and complements recent results of several authors.
16023484
Asymptotically convergent modified recursive least-squares with data-dependent updating and forgetting factor
Continual updating of estimates required by most recursive estimation schemes often involves redundant usage of information and may result in system instabilities in the presence of bounded output disturbances. This paper investigates an algorithm which has the capability of eliminating these difficulties. Based on a set theoretic assumption, the algorithm yields modified least-squares estimates with a forgetting factor. It updates the estimates selectively depending on whether the observed data contain sufficient information. The information evaluation required at each step involves very simple computations. In addition, the parameter estimates are shown to converge asymptotically to a region around the true parameter at an exponential rate.
5977047
Decision tree evolution using limited number of labeled data items from drifting data streams
Most previously proposed mining methods on data streams make an unrealistic assumption that "labelled" data stream is readily available and can be mined at anytime. However, in most real-world problems, labelled data streams are rarely immediately available. Due to this reason, models are reconstructed only when labelled data become available periodically. This passive stream mining model has several drawbacks. We propose a concept of demand-driven active data mining. In active mining, the loss of the model is either continuously guessed without using any true class labels or estimated, whenever necessary, from a small number of instances whose actual class labels are verified by paying an affordable cost. When the estimated loss is more than a tolerable threshold, the model evolves by using a small number of instances with verified true class labels. Previous work on active mining concentrates on error guess and estimation. In this paper, we discuss several approaches on decision tree evolution.
10038411
On personal data license design and negotiation
Web applications often require users to provide personal information for customization, identification, or completing a transaction. However, the way we disclose our information in current cyberspace does not allow us to control the usage, release and circulation of the information, violating the basic principle of personal information privacy. Online personal data licensing (OPDL) is a framework to bring the control of personal information back to individuals by requiring application and service providers to obtain a license from a person before collecting, processing, and using his personal information. In this paper we present the license design and automation of the license negotiation process.
41663728
The JAVELIN Question-Answering System at TREC 2002
This Conference Proceeding is brought to you for free and open access by the School of Computer Science at Research Showcase. It has been acceptedfor inclusion in Computer Science Department by an authorized administrator of Research Showcase. For more information, please contactkbehrman@andrew.cmu.edu.