Oracle’s objective in TREC-10 was to study the behavior of Oracle information retrieval in previously unexplored application areas. The software used was Oracle9i Text[1], Oracle’s full-text retrieval engine integrated with the Oracle relational database management system, and the Oracle PL

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This paper describes the JAVELIN approach for open-domain question answering (Justification-based Answer Valuation through Language Interpretation), and our participation in the TREC 2002 question-answering track. The main scientific tenets underlying JAVELIN are: QA as Planning. Question Answering

In this paper, we describe our experimentations in evaluating answer formulation for question-answering (QA) systems. In the context of QA, answer formulation can serve two purposes: improving answer extraction or improving human-computer interaction (HCI). Each purpose has different precision

The TREC question answering track is an effort to bring the benefits of loxge-scale evaluation to beox on the question answering problem. In its third yeox, the track continued to focus on retrieving small snippets of text that contain an answer to a question. However, several new conditions were

search strategies, each of which is a query expansion for a given domain. For this task, we compare the performance of RankBoost to the individual search strategies. The second experiment is a collaborative-filtering task for making movie recommendations. Here, we present results comparing Rank

Abstract. This paper addresses the problem of improving the accuracy of an hypothesis output by a learning algorithm in the distribution-free (PAC) learning model. A concept class is learnable (or strongly learnable) if, given access to a Source of examples of the unknown concept, the learner with high probability is able to output an hypothesis that is correct on all but an arbitrarily small fraction of the instances. The concept class is weakly learnable if the learner can produce an hypothesis that performs only slightly better than random guessing. In this paper, it is shown that these two notions of learnability are equivalent. A method is described for converting a weak learning algorithm into one that achieves arbitrarily high accuracy. This construction may have practical applications as a tool for efficiently converting a mediocre learning algorithm into one that performs extremely well. In addition, the construction has some interesting theoretical consequences, including a set of general upper bounds on the complexity of any strong learning algorithm as a function of the allowed error e.

The TREC-8 Question Answering track was the first large-scale evaluation of domain-independent question answering systems. This paper summarizes the results of the track by giving a brief overview of the different approaches taken to solve the problem. The most accurate systems found a correct

. We present the lifting scheme, a simple construction of second generation wavelets, wavelets that are not necessarily translates and dilates of one fixed function. Such wavelets can be adapted to intervals, domains, surfaces, weights, and irregular samples. We show how the lifting scheme leads to a faster, in-place calculation of the wavelet transform. Several examples are included. Key words. wavelet, multiresolution, second generation wavelet, lifting scheme AMS subject classifications. 42C15 1. Introduction. Wavelets form a versatile tool for representing general functions or data sets. Essentially we can think of them as data building blocks. Their fundamental property is that they allow for representations which are efficient and which can be computed fast. In other words, wavelets are capable of quickly capturing the essence of a data set with only a small set of coefficients. This is based on the fact that most data sets have correlation both in time (or space) and frequenc...

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