13868805
Architecture of a metasearch engine that supports user information needs
When a query is submitted to a metasearch engine, decisions are made with respect to the underlying search engines to be used, what modifications will be made to the query, and how to score the results. These decisions are typically made by considering only the user's keyword query, neglecting the larger information need. Users with specific needs, such as “research papers” or “homepages,” are not able to express these needs in a way that affects the decisions made by the metasearch engine. In this paper, we describe a metasearch engine architecture that considers the user's information need for each decision. Users with different needs, but the same keyword query, may search different sub-search engines, have different modifications made to their query, and have results ordered differently. Our architecture combines several powerful approaches together in a single general purpose metasearch engine.
17331455
Rule Revision With Recurrent Neural Networks
Recurrent neural networks readily process, recognize and generate temporal sequences. By encoding grammatical strings as temporal sequences, recurrent neural networks can be trained to behave like deterministic sequential finite-state automata. Algorithms have been developed for extracting grammatical rules from trained networks. Using a simple method for inserting prior knowledge (or rules) into recurrent neural networks, we show that recurrent neural networks are able to perform rule revision. Rule revision is performed by comparing the inserted rules with the rules in the finite-state automata extracted from trained networks. The results from training a recurrent neural network to recognize a known non-trivial, randomly-generated regular grammar show that not only do the networks preserve correct rules but that they are able to correct through training inserted rules which were initially incorrect (i.e. the rules were not the ones in the randomly generated grammar).
30079691
Evaluating Answer Quality/Efficiency Tradeoffs
974804
Predicting Multiprocessor Memory Access Patterns with Learning Models
Machine learning techniques are applicable to computer system optimization. We show that shared memory multiprocessors can successfully utilize machine learning algorithms for memory access pattern prediction. In particular three different on-line machine learning prediction techniques were tested to learn and predict repetitive memory access patterns for three typical parallel processing applications, the 2-D relaxation algorithm, matrix multiply and Fast Fourier Transform on a shared memory multiprocessor. The predictions were then used by a routing control algorithm to reduce control latency in the interconnection network by configuring the interconnection network to provide needed memory access paths before they were requested. Three trainable prediction techniques were used and tested: 1). a Markov predictor, 2). a linear predictor and 3). a time delay neural network (TDNN) predictor. Different predictors performed best on different applications, but the TDNN produced uniformly good results.
3531625
Autonomous citation matching
Advances in computational resources and the communications infrastructure, and the rapid rise of the World Wide Web, have led to the increasingly widespread availability of scientific papers in electronic form. Scientific papers usually contain citations to previous work, and indices of these citations are valuable for literature search , analysis, and evaluation. Current citation indices of the scientific literature are constructed using manual effort and are typically expensive. Part of the reason for using manual effort is the great variability of citation syntax – i t can be difficult to autonomously determine if two citations refer to the same article because citations can be written in many different formats. We present machine learning techniques that identify variant forms of citations to the same paper. A number of algorithms are presented. An algorithm based on word and phrase matching is found to perform best, and is sufficiently accurate for unassisted use in an autonomous citation indexing system. An algorithm based on a string edit distance performs poorly in comparison. A computationally efficient subfield algorithm is also presented. The accuracy and efficiency of all algorithms is quantitatively compared on a number of datasets.
14903074
Stable Encoding of Large Finite-State Automata in Recurrent Neural Networks with Sigmoid Discriminants
We propose an algorithm for encoding deterministic finite-state automata (DFAs) in second-order recurrent neural networks with sigmoidal discriminant function and we prove that the languages accepted by the constructed network and the DFA are identical. The desired finite-state network dynamics is achieved by programming a small subset of all weights. A worst case analysis reveals a relationship between the weight strength and the maximum allowed network size, which guarantees finite-state behavior of the constructed network. We illustrate the method by encoding random DFAs with 10, 100, and 1000 states. While the theory predicts that the weight strength scales with the DFA size, we find empirically the weight strength to be almost constant for all the random DFAs. These results can be explained by noting that the generated DFAs represent average cases. We empirically demonstrate the existence of extreme DFAs for which the weight strength scales with DFA size.
39384561
Next generation CiteSeer
CiteSeer, a computer and information science search engine and digital library, has been a radical departure for scientific document access and analysis. With nearly 700,000 documents, it has sometimes two million page views a day making it one of the most popular document access engines in science. CiteSeer is also portable, having been extended to ebusiness (eBizSearch) and more recently to academic business documents (SMEALSearch). CiteSeer is based on two features: actively acquiring new documents and automatic tagging and linking of metadata information inherent in an academic document's syntactic structure. Why is CiteSeer so popular? We discuss this and methods for providing new tagged metadata such as institutions and acknowledgements, new data resources and services and the issues in automation. We then discuss the next generation of CiteSeer.