16,602 research outputs found
Linguistic Geometries for Unsupervised Dimensionality Reduction
Text documents are complex high dimensional objects. To effectively visualize
such data it is important to reduce its dimensionality and visualize the low
dimensional embedding as a 2-D or 3-D scatter plot. In this paper we explore
dimensionality reduction methods that draw upon domain knowledge in order to
achieve a better low dimensional embedding and visualization of documents. We
consider the use of geometries specified manually by an expert, geometries
derived automatically from corpus statistics, and geometries computed from
linguistic resources.Comment: 13 pages, 15 figure
XML Schema Clustering with Semantic and Hierarchical Similarity Measures
With the growing popularity of XML as the data representation language, collections of the XML data are exploded in numbers. The methods are required to manage and discover the useful information from them for the improved document handling. We present a schema clustering process by organising the heterogeneous XML schemas into various groups. The methodology considers not only the linguistic and the context of the elements but also the hierarchical structural similarity. We support our findings with experiments and analysis
ImageSieve: Exploratory search of museum archives with named entity-based faceted browsing
Over the last few years, faceted search emerged as an attractive alternative to the traditional "text box" search and has become one of the standard ways of interaction on many e-commerce sites. However, these applications of faceted search are limited to domains where the objects of interests have already been classified along several independent dimensions, such as price, year, or brand. While automatic approaches to generate faceted search interfaces were proposed, it is not yet clear to what extent the automatically-produced interfaces will be useful to real users, and whether their quality can match or surpass their manually-produced predecessors. The goal of this paper is to introduce an exploratory search interface called ImageSieve, which shares many features with traditional faceted browsing, but can function without the use of traditional faceted metadata. ImageSieve uses automatically extracted and classified named entities, which play important roles in many domains (such as news collections, image archives, etc.). We describe one specific application of ImageSieve for image search. Here, named entities extracted from the descriptions of the retrieved images are used to organize a faceted browsing interface, which then helps users to make sense of and further explore the retrieved images. The results of a user study of ImageSieve demonstrate that a faceted search system based on named entities can help users explore large collections and find relevant information more effectively
CHORUS Deliverable 2.2: Second report - identification of multi-disciplinary key issues for gap analysis toward EU multimedia search engines roadmap
After addressing the state-of-the-art during the first year of Chorus and establishing the existing landscape in
multimedia search engines, we have identified and analyzed gaps within European research effort during our second year.
In this period we focused on three directions, notably technological issues, user-centred issues and use-cases and socio-
economic and legal aspects. These were assessed by two central studies: firstly, a concerted vision of functional breakdown
of generic multimedia search engine, and secondly, a representative use-cases descriptions with the related discussion on
requirement for technological challenges. Both studies have been carried out in cooperation and consultation with the
community at large through EC concertation meetings (multimedia search engines cluster), several meetings with our
Think-Tank, presentations in international conferences, and surveys addressed to EU projects coordinators as well as
National initiatives coordinators. Based on the obtained feedback we identified two types of gaps, namely core
technological gaps that involve research challenges, and “enablers”, which are not necessarily technical research
challenges, but have impact on innovation progress. New socio-economic trends are presented as well as emerging legal
challenges
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
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