Comment: Classifier Technology and the Illusion of Progress--Credit Scoring

Comment on Classifier Technology and the Illusion of Progress--Credit Scoring [math.ST/0606441]Comment: Published at http://dx.doi.org/10.1214/088342306000000051 in the Statistical Science (http://www.imstat.org/sts/) by the Institute of Mathematical Statistics (http://www.imstat.org

Noise-tolerant approximate blocking for dynamic real-time entity resolution

Entity resolution is the process of identifying records in one or multiple data sources that represent the same real-world entity. This process needs to deal with noisy data that contain for example wrong pronunciation or spelling errors. Many real world applications require rapid responses for entity queries on dynamic datasets. This brings challenges to existing approaches which are mainly aimed at the batch matching of records in static data. Locality sensitive hashing (LSH) is an approximate blocking approach that hashes objects within a certain distance into the same block with high probability. How to make approximate blocking approaches scalable to large datasets and effective for entity resolution in real-time remains an open question. Targeting this problem, we propose a noise-tolerant approximate blocking approach to index records based on their distance ranges using LSH and sorting trees within large sized hash blocks. Experiments conducted on both synthetic and real-world datasets show the effectiveness of the proposed approach

Vector Symbolic Architectures answer Jackendoff's challenges for cognitive neuroscience

Jackendoff (2002) posed four challenges that linguistic combinatoriality and rules of language present to theories of brain function. The essence of these problems is the question of how to neurally instantiate the rapid construction and transformation of the compositional structures that are typically taken to be the domain of symbolic processing. He contended that typical connectionist approaches fail to meet these challenges and that the dialogue between linguistic theory and cognitive neuroscience will be relatively unproductive until the importance of these problems is widely recognised and the challenges answered by some technical innovation in connectionist modelling. This paper claims that a little-known family of connectionist models (Vector Symbolic Architectures) are able to meet Jackendoff's challenges.Comment: This is a slightly updated version of the paper presented at the Joint International Conference on Cognitive Science, 13-17 July 2003, University of New South Wales, Sydney, Australia. 6 page

Towards Scalable Real-Time Entity Resolution using a Similarity-Aware Inverted Index Approach

Most research into entity resolution (also known as record linkage or data matching) has concentrated on the quality of the matching results. In this paper, we focus on matching time and scalability, with the aim to achieve large-scale real-time entity resolution. Traditional entity resolution techniques have as-sumed the matching of two static databases. In our networked and online world, however, it is becoming increasingly important for many organisations to be able to conduct entity resolution between a collection of often very large databases and a stream of query or update records. The matching should be done in (near) real-time, and be as automatic and accurate as possible, returning a ranked list of matched records for each given query record. This task therefore be-comes similar to querying large document collections, as done for example by Web search engines, however based on a different type of documents: structured database records that, for example, contain personal information, such as names and addresses. In this paper, we investigate inverted indexing techniques, as commonly used in Web search engines, and employ them for real-time entity resolution. We present two variations of the traditional inverted in-dex approach, aimed at facilitating fast approximate matching. We show encouraging initial results on large real-world data sets, with the inverted index ap-proaches being up-to one hundred times faster than the traditionally used standard blocking approach. However, this improved matching speed currently comes at a cost, in that matching quality for larger data sets can be lower compared to when tandard blocking is used, and thus more work is required

Fermi motion and quark off-shellness in elastic vector- meson production

Peer reviewe

A Search for Selectrons and Squarks at HERA

Data from electron-proton collisions at a center-of-mass energy of 300 GeV are used for a search for selectrons and squarks within the framework of the minimal supersymmetric model. The decays of selectrons and squarks into the lightest supersymmetric particle lead to final states with an electron and hadrons accompanied by large missing energy and transverse momentum. No signal is found and new bounds on the existence of these particles are derived. At 95% confidence level the excluded region extends to 65 GeV for selectron and squark masses, and to 40 GeV for the mass of the lightest supersymmetric particle.Comment: 13 pages, latex, 6 Figure

Multiplicative Binding, Representation Operators & Analogy (Workshop Poster)

Analogical inference depends on systematic substitution of the components of compositional structures. Simple systematic substitution has been achieved in a number of connectionist systems that support binding (the ability to create connectionist representations of the combination of component representations). These systems have used various implementations of two generic composition operators: bind() and bundle(). This paper introduces a novel implementation of the bind() operator that is simple, can be efficiently implemented, and highlights the relationship between retrieval queries and analogical mapping. A frame of role/filler bindings can easily be represented using bind() and bundle(). However, typical binding systems are unable to adequately represent multiple frames and arbitrary nested compositional structures. A novel family of representational operators (called braid()) is introduced to address these problems. Other binding systems make the strong assumption that the roles and fillers are disjoint in order to avoid ambiguities inherent in their representational idioms. The braid() operator can be used to avoid this assumption. The new representational idiom suggests how the cognitive processes of bottom-up and top-down object recognition might be implemented. These processes depend on analogical mapping to integrate disjoint representations and drive perceptual search

Adaptive Temporal Entity Resolution on Dynamic Databases

Entity resolution is the process of matching records that refer to the same entities from one or several databases in situations where the records to be matched do not include unique entity identifiers. Matching therefore has to rely upon partially ident