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.
14572455
Effects of Noise on Convergence and Generalization in Recurrent Networks
We introduce and study methods of inserting synaptic noise into dynamically-driven recurrent neural networks and show that applying a controlled amount of noise during training may improve convergence and generalization. In addition, we analyze the effects of each noise parameter (additive vs. multiplicative, cumulative vs. non-cumulative, per time step vs. per string) and predict that best overall performance can be achieved by injecting additive noise at each time step. Extensive simulations on learning the dual parity grammar from temporal strings substantiate these predictions.
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.
13318416
Context and Page Analysis for Improved Web Search
NECI Research Institute has developed a metasearch engine that improves the efficiency of Web searches by downloading and analyzing each document and then displaying results that show the query terms in concert. The paper discusses the features of the NECI metasearch engine.
26945654
The complexity of language recognition by neural networks
Abstract Neural networks are frequently used as adaptive classifiers. This research represents an attempt to measure the “neural complexity” of any regular set of binary strings, that is, to quantify the size of a recurrent continuous-valued neural network that is needed for correctly classifying the given regular set. Our estimate provides a predictor that is superior to the size of the minimal automaton that was used as an upper bound so far. Moreover, it is easily computable, using techniques from the theory of rational power series in non-commuting variables.
6095811
Experimental Comparison of the Effect of Order in Recurrent Neural Networks
There has been much interest in increasing the computational power of neural networks. In addition there has been much interest in “designing” neural networks better suited to particular problems. Increasing the “order” of the connectivity of a neural network permits both. Though order has played a significant role in feedforward neural networks, its role in dynamically driven recurrent networks is still being understood. This work explores the effect of order in learning grammars. We present an experimental comparison of first order and second order recurrent neural networks, as applied to the task of grammatical inference. We show that for the small grammars studied these two neural net architectures have comparable learning and generalization power, and that both are reasonably capable of extracting the correct finite state automata for the language in question. However, for a larger randomly-generated ten-state grammar, second order networks significantly outperformed the first order networks, both in convergence time and generalization capability. We show that these networks learn faster the more neurons they have (our experiments used up to 10 hidden neurons), but that the solutions found by smaller networks are usually of better quality (in terms of generalization performance after training). Second order nets have the advantage that they converge more quickly to a solution and can find it more reliably than first order nets, but that the second order solutions tend to be of poorer quality than those of the first order if both architectures are trained to the same error tolerance. Despite this, second order nets can more successfully extract finite state machines using heuristic clustering techniques applied to the internal state representations. We speculate that this may be due to restrictions on the ability of first order architecture to fully make use of its internal state representation power and that this may have implications for the performance of the two architectures when scaled up to larger problems.
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.