The twelfth Text REtrieval Conference, TREC 2003, was held at the National Institute of Standards and Technology (NIST) November 18–21, 2003. The conference was co-sponsored by NIST, the US Department of Defense Advanced Research and Development Activity (ARDA), and the Defense Advanced Research Projects Agency (DARPA). TREC 2003 is the latest in a series of workshops designed to foster researc...

The increasing prevalence of broadband Internet access is making it easier to obtain rich contents like images, and more people are attempting image retrieval. We focus on how to present web image retrieval results to users. Most retrieval results contain multiple topics. To offset this complexity, many papers have discussed text retrieval result clustering [11][14]. In result clustering, we cl...

In this notebook, we describe the automatic retrieval runs from NEC Laboratories America (NECLA) for the Text REtrieval Conference (TREC) 2012 Medical Records track. Our approach is based on a combination of UMLS medical concept detection and a set of simple retrieval models. Our best run, sennamed2, has achieved the best inferred average precision (infAP) score on 5 of the 47 test topics, and ...

The 2001 Text Retrieval Conference will include a large-scale evaluation of systems designed to retrieve Arabic documents using English, French or Arabic queries.

The sixteenth Text REtrieval Conference, TREC 2007, was held at the National Institute of Standards and Technology (NIST) November 6–9, 2007. The conference was co-sponsored by NIST and the Intelligence Advanced Research Projects Activity (IARPA). TREC 2007 had 95 participating groups from 18 countries. Table 2 at the end of the paper lists the participating groups. TREC 2007 is the latest in a...

In November of 1992 the first Text REtrieval Conference (TREC-1) was held at NIST (Harman 1993). This conference, co-sponsored by ARPA and NIST, brought together information retrieval researchers to discuss their system results on the new TIPSTER test collection. This was the first time that such groups had ever compared results on the same data using the same evaluation methods, and represente...

This paper presents the results of the University at Buffalo in the 2006 ImageCLEFmed task. Our approach for this task combines Content Based Image Retrieval (CBIR) and text retrieval to improve retrieval of medical images. Our results are comparable to other approaches presented in the task. Our results show that text retrieval performs well across the three different types of topics (visual, ...

This paper presents the results of the State University of New York at Buffalo in the Cross Language Evaluation Forum 2005 (CLEF 2005). We participated in monolingual Portuguese, bilingual EnglishPortuguese and in the medical image retrieval tasks. We used the SMART retrieval system for text retrieval in the mono and bilingual retrieval tasks on Portuguese documents. The main goal of this part ...

This article presents an information retrieval system (IRS) using genetic algorithm to increase the performance and efficiency of Text to Matrix Generator (TMG).This paper presents an extension in previous work of Text to Matrix Generator (TMG). In this paper, we proposed a genetic algorithm approach in Text to Matrix Generator for information retrieval. This experimental result shows an improv...