Institution
Dublin City University
Education•Dublin, Ireland•
About: Dublin City University is a education organization based out in Dublin, Ireland. It is known for research contribution in the topics: Machine translation & Laser. The organization has 5904 authors who have published 17178 publications receiving 389376 citations. The organization is also known as: National Institute for Higher Education, Dublin & DCU.
Topics: Machine translation, Laser, Irish, Population, Context (language use)
Papers published on a yearly basis
Papers
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TL;DR: Experimental results indicate that the incorporation of colour information enhances the performance of the texture analysis techniques examined, and the classification accuracy is determined using a neural network classifier based on Learning Vector Quantization.
230 citations
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18 Aug 1996TL;DR: This paper describes the first successful application of semantically-based word-word similarities, work described elsewhere, to an information retrieval application, specifically the indexing and retrieval of captions describing the content of images.
Abstract: Traditional approaches to information retrieval are based upon representing a user’s query as a bag of query terms and a document as a bag of index terms and computing a degree of similarity between the two based on the overlap or number of query terms in common between them. Our long-term approach to IR applications is based upon precomputing semantically-based word-word similarities, work which is described elsewhere, and using these as part of the document-query similarity measure. A basic premise of our word-to-word similarity measure is that the input to this computation is the correct or intended word sense but in information retrieval applications, automatic and accurate word sense dkambiguation remains an unsolved problem. In this paper we describe our first successful application of these ideas to an information retrieval application, specifically the indexing and retrieval of captions describing the content of images. We have hand-captioned 2714 images and to circumvent, for the time being, the problems raised by word sense disambiguation, we manually disambiguated polysemous words in captions. We have also built a Collection of 60 queries and for each, determined relevance assessments. Using this environment we were able to run experiments in which we varied how the query-caption similarity measure used our pre-computed word-word semantic distances. Our experiments, reported in the paper, show significant improvement for this environment over the more traditional approaches to information retrieval.
229 citations
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19 Jul 2009TL;DR: This work proposes and proposes and studies the effectiveness of three methods for expansion term selection, each modeling the Wikipedia based pseudo-relevance information from a different perspective, and incorporates the expansion terms into the original query and uses language modeling IR to evaluate these methods.
Abstract: Pseudo-relevance feedback (PRF) via query-expansion has been proven to be e®ective in many information retrieval (IR) tasks. In most existing work, the top-ranked documents from an initial search are assumed to be relevant and used for PRF. One problem with this approach is that one or more of the top retrieved documents may be non-relevant, which can introduce noise into the feedback process. Besides, existing methods generally do not take into account the significantly different types of queries that are often entered into an IR system. Intuitively, Wikipedia can be seen as a large, manually edited document collection which could be exploited to improve document retrieval effectiveness within PRF. It is not obvious how we might best utilize information from Wikipedia in PRF, and to date, the potential of Wikipedia for this task has been largely unexplored. In our work, we present a systematic exploration of the utilization of Wikipedia in PRF for query dependent expansion. Specifically, we classify TREC topics into three categories based on Wikipedia: 1) entity queries, 2) ambiguous queries, and 3) broader queries. We propose and study the effectiveness of three methods for expansion term selection, each modeling the Wikipedia based pseudo-relevance information from a different perspective. We incorporate the expansion terms into the original query and use language modeling IR to evaluate these methods. Experiments on four TREC test collections, including the large web collection GOV2, show that retrieval performance of each type of query can be improved. In addition, we demonstrate that the proposed method out-performs the baseline relevance model in terms of precision and robustness.
229 citations
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TL;DR: The chemical and genetic strategies are complementary and have been combined to stabilize cytochrome c by metal-mediated cross-linking following site-specific mutagenesis and the beneficial effects of certain additives on protein stability are summarized.
229 citations
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TL;DR: It is concluded that the information which is derived from in vivo sampling of the uterus in clinically normal cows during the first 7 weeks pp.
229 citations
Authors
Showing all 6059 results
Name | H-index | Papers | Citations |
---|---|---|---|
Joseph Wang | 158 | 1282 | 98799 |
David Cameron | 154 | 1586 | 126067 |
David Taylor | 131 | 2469 | 93220 |
Gordon G. Wallace | 114 | 1267 | 69095 |
David A. Morrow | 113 | 598 | 56776 |
G. Hughes | 103 | 957 | 46632 |
David Wilson | 102 | 757 | 49388 |
Muhammad Imran | 94 | 3053 | 51728 |
Haibo Zeng | 94 | 604 | 39226 |
David Lloyd | 90 | 1017 | 37691 |
Vikas Kumar | 89 | 859 | 39185 |
Luke P. Lee | 84 | 413 | 22803 |
James Chapman | 82 | 483 | 36468 |
Muhammad Iqbal | 77 | 961 | 23821 |
Michael C. Berndt | 76 | 228 | 16897 |