IR group of Tsinghua University participated in the expert finding task of TREC2005 enterprise track this year. We developed a novel method which is called document reorganization to solve the problem of locating expert for certain query topics. This method collects and combines related information

In 2006, IBM participated for the first time in the Enterprise Track, submitting runs for both the discussion and expert tasks. The Enterprise Track is intended to address information seeking tasks common in corporate settings using information that is readily available on corporate intranets

The TREC-5 filtering track, an evaluation of binary text classification systems, was a repeat of the filtering evaluation run in a trial version for TREC-4, with only the data set and participants changing. Seven sites took part, submitting a total of ten runs. We review the nature of the task

This paper gives an overview of the work done at the

with the growth of new information needs (e.g., question-answering, cross-lingual), data types (e.g., video) and platforms (e.g., the Web). This paper describes the events leading up to the first year of TREC Genomics Track, the first year’s results, and future directions for subsequent years. Genomics

The novelty track was first introduced in TREC 2002.Given a TREC topic and an ordered list of documents, systems must find the relevant and novelsentences that should be returned to the user from this set. This task integrates aspects of passage re-trieval and information filtering. This year

mbination and 3) The tuning and ranking framework we used this year. To calculate BM25F [4] we first calculate a normalised term frequency for each field: x d,f,t := x d,f,t (1 +B f ( l d,f l f - 1)) # {SUBJECT, BODY, QUOTED} indicates the field type, x d,f,t is the term frequency of term t in the field type f of document d, l d,f is the length of that field, and l f is the average field length for that field type. B f is a field-dependant parameter similar to the B parameter in BM25. In particular, if B f = 0 there is no normalisation and if B f = 1 the frequency is completely normalised w.r.t. the average field length. These term frequencies can then be combined in a linearly weighted sum to obtain the final term pseudo-frequency: x d,t = W f x d,f,t (2) with weight parameters W f . This is then used in the usual BM25 saturating function. This leads the following ranking function, which we refer to as BM25F: BM25F (d) := t#q#d x d,t K 1 + x d,t t (3)

This paper is an introduction to the track framework -- the tasks, data, and measures -- and the approaches taken. An overview of results will be presented in the plenary session. For detailed information about approaches and results see the various site reports and the back-of-the-notebook results

The University of Pittsburgh team participated in two tracks for TREC 2005: the High Accuracy Retrieval from Documents (HARD) track and the Enterprise Retrieval track.

The TREC 2005 Genomics Track featured two tasks, an ad hoc retrieval task and four subtasks in text categorization. The ad hoc retrieval task utilized a 10-year, 4.5-million document subset of the MEDLINE bibliographic database, with 50 topics conforming to five generic topic types