reSearch : enhancing information retrieval with images

Combining image and text search is an open research question. The main issues are what technologies to base this solution on, and what measures of relevance to employ. Our reSearch prototype mashes up papers indexed using information retrieval techniques (Terrier) with Google image search for faces and Google book search. The user can interactively employ query expansion with additional terms suggested by Terrier, and use those terms to expand both the text and image search. We test this solution with a selection of recent publications and queries concerning people engaged in research. We report on the effectiveness of this solution. It seems that the combination works to a large extent, as testified by our observations

Learning to Extract Keyphrases from Text

Many academic journals ask their authors to provide a list of about five to fifteen key words, to appear on the first page of each article. Since these key words are often phrases of two or more words, we prefer to call them keyphrases. There is a surprisingly wide variety of tasks for which keyphrases are useful, as we discuss in this paper. Recent commercial software, such as Microsoft?s Word 97 and Verity?s Search 97, includes algorithms that automatically extract keyphrases from documents. In this paper, we approach the problem of automatically extracting keyphrases from text as a supervised learning task. We treat a document as a set of phrases, which the learning algorithm must learn to classify as positive or negative examples of keyphrases. Our first set of experiments applies the C4.5 decision tree induction algorithm to this learning task. The second set of experiments applies the GenEx algorithm to the task. We developed the GenEx algorithm specifically for this task. The third set of experiments examines the performance of GenEx on the task of metadata generation, relative to the performance of Microsoft?s Word 97. The fourth and final set of experiments investigates the performance of GenEx on the task of highlighting, relative to Verity?s Search 97. The experimental results support the claim that a specialized learning algorithm (GenEx) can generate better keyphrases than a general-purpose learning algorithm (C4.5) and the non-learning algorithms that are used in commercial software (Word 97 and Search 97)

Data Mining in Electronic Commerce

Modern business is rushing toward e-commerce. If the transition is done properly, it enables better management, new services, lower transaction costs and better customer relations. Success depends on skilled information technologists, among whom are statisticians. This paper focuses on some of the contributions that statisticians are making to help change the business world, especially through the development and application of data mining methods. This is a very large area, and the topics we cover are chosen to avoid overlap with other papers in this special issue, as well as to respect the limitations of our expertise. Inevitably, electronic commerce has raised and is raising fresh research problems in a very wide range of statistical areas, and we try to emphasize those challenges.Comment: Published at http://dx.doi.org/10.1214/088342306000000204 in the Statistical Science (http://www.imstat.org/sts/) by the Institute of Mathematical Statistics (http://www.imstat.org

Evaluation of MIRACLE approach results for CLEF 2003

This paper describes MIRACLE (Multilingual Information RetrievAl for the CLEf campaign) approach and results for the mono, bi and multilingual Cross Language Evaluation Forum tasks. The approach is based on the combination of linguistic and statistic techniques to perform indexing and retrieval tasks

Can Automatic Abstracting Improve on Current Extracting Techniques in Aiding Users to Judge the Relevance of Pages in Search Engine Results?

Current search engines use sentence extraction techniques to produce snippet result summaries, which users may find less than ideal for determining the relevance of pages. Unlike extracting, abstracting programs analyse the context of documents and rewrite them into informative summaries. Our project aims to produce abstracting summaries which are coherent and easy to read thereby lessening users’ time in judging the relevance of pages. However, automatic abstracting technique has its domain restriction. For solving this problem we propose to employ text classification techniques. We propose a new approach to initially classify whole web documents into sixteen top level ODP categories by using machine learning and a Bayesian classifier. We then manually create sixteen templates for each category. The summarisation techniques we use include a natural language processing techniques to weight words and analyse lexical chains to identify salient phrases and place them into relevant template slots to produce summaries

Report of MIRACLE team for the Ad-Hoc track in CLEF 2006

This paper presents the 2006 MIRACLE’s team approach to the AdHoc Information Retrieval track. The experiments for this campaign keep on testing our IR approach. First, a baseline set of runs is obtained, including standard components: stemming, transforming, filtering, entities detection and extracting, and others. Then, a extended set of runs is obtained using several types of combinations of these baseline runs. The improvements introduced for this campaign have been a few ones: we have used an entity recognition and indexing prototype tool into our tokenizing scheme, and we have run more combining experiments for the robust multilingual case than in previous campaigns. However, no significative improvements have been achieved. For the this campaign, runs were submitted for the following languages and tracks: - Monolingual: Bulgarian, French, Hungarian, and Portuguese. - Bilingual: English to Bulgarian, French, Hungarian, and Portuguese; Spanish to French and Portuguese; and French to Portuguese. - Robust monolingual: German, English, Spanish, French, Italian, and Dutch. - Robust bilingual: English to German, Italian to Spanish, and French to Dutch. - Robust multilingual: English to robust monolingual languages. We still need to work harder to improve some aspects of our processing scheme, being the most important, to our knowledge, the entities recognition and normalization

From Frequency to Meaning: Vector Space Models of Semantics

Computers understand very little of the meaning of human language. This profoundly limits our ability to give instructions to computers, the ability of computers to explain their actions to us, and the ability of computers to analyse and process text. Vector space models (VSMs) of semantics are beginning to address these limits. This paper surveys the use of VSMs for semantic processing of text. We organize the literature on VSMs according to the structure of the matrix in a VSM. There are currently three broad classes of VSMs, based on term-document, word-context, and pair-pattern matrices, yielding three classes of applications. We survey a broad range of applications in these three categories and we take a detailed look at a specific open source project in each category. Our goal in this survey is to show the breadth of applications of VSMs for semantics, to provide a new perspective on VSMs for those who are already familiar with the area, and to provide pointers into the literature for those who are less familiar with the field