Text Retrieval Conference

• Encourage retrieval search based on large text collections
• Increase communication among industry, academia, and government by creating an open forum for the exchange of research ideas
• Speed the transfer of technology from research labs into commercial products by demonstrating substantial improvements retrieval methodologies on real world problems
• To increase the availability of appropriate evaluation techniques for use by industry and academia including development of new evaluation techniques more applicable to current systems

TREC is overseen by a program committee consisting of representatives from government, industry, and academia. For each TREC, NIST provide a set of documents and questions. Participants run their own retrieval system on the data and return to NIST a list of retrieved top-ranked documents .NIST pools the individual result judges the retrieved documents for correctness and evaluates the results. The TREC cycle ends with a workshop that is a forum for participants to share their experiences.

TREC defines relevance as: "If you were writing a report on the subject of the topic and would use the information contained in the document in the report, then the document is relevant."[3] Most TREC retrieval tasks use binary relevance: a document is either relevant or not relevant. Some TREC tasks use graded relevance, capturing multiple degrees of relevance. Most TREC collections are too large to perform complete relevance assessment; for these collections it is impossible to calculate the absolute recall for each query. To decide which documents to assess, TREC usually uses a method call pooling. In this method, the top-ranked n documents from each contributing run are aggregated, and the resulting document set is judged completely.

In 1992 TREC-1 was held at NIST. The first conference attracted 28 groups of researchers from academia and industry. It demonstrated a wide range of different approaches to the retrieval of text from large document collections .Finally TREC1 revealed the facts that automatic construction of queries from natural language query statements seems to work. Techniques based on natural language processing were no better no worse than those based on vector or probabilistic approach.

TREC2 Took place in August 1993. 31 group of researchers participated in this. Two types of retrieval were examined. Retrieval using an ‘ad hoc’ query and retrieval using a ‘routing' query

In TREC-3 a small group experiments worked with Spanish language collection and others dealt with interactive query formulation in multiple databases

TREC-4 they made even shorter to investigate the problems with very short user statements

TREC-5 includes both short and long versions of the topics with the goal of carrying out deeper investigation into which types of techniques work well on various lengths of topics

In TREC-6 Three new tracks speech, cross language, high precision information retrieval were introduced. The goal of cross language information retrieval is to facilitate research on system that are able to retrieve relevant document regardless of language of the source document

TREC-7 contained seven tracks out of which two were new Query track and very large corpus track. The goal of the query track was to create a large query collection

TREC-8 contain seven tracks out of which two –question answering and web tracks were new. The objective of QA query is to explore the possibilities of providing answers to specific natural language queries

TREC-9 Includes seven tracks

In TREC-10 Video tracks introduced Video tracks design to promote research in content based retrieval from digital video

In TREC-11 Novelty tracks introduced. The goal of novelty track is to investigate systems abilities to locate relevant and new information within the ranked set of documents returned by a traditional document retrieval system

TREC-12 held in 2003 added three new tracks; Genome track, robust retrieval track, HARD (Highly Accurate Retrieval from Documents) [4]

NIST claims that within the first six years of the workshops, the effectiveness of retrieval systems approximately doubled.[7] The conference was also the first to hold large-scale evaluations of non-English documents, speech, video and retrieval across languages. Additionally, the challenges have inspired a large body of publications. Technology first developed in TREC is now included in many of the world's commercial search engines. An independent report by RTII found that "about one-third of the improvement in web search engines from 1999 to 2009 is attributable to TREC. Those enhancements likely saved up to 3 billion hours of time using web search engines. ... Additionally, the report showed that for every $1 that NIST and its partners invested in TREC, at least $3.35 to $5.07 in benefits were accrued to U.S. information retrieval researchers in both the private sector and academia." [8] [9]

While one study suggests that the state of the art for ad hoc search did not advance substantially in the decade preceding 2009,[10] it is referring just to search for topically relevant documents in small news and web collections of a few gigabytes. There have been advances in other types of ad hoc search. For example, test collections were created for known-item web search which found improvements from the use of anchor text, title weighting and url length, which were not useful techniques on the older ad hoc test collections. In 2009, a new billion-page web collection was introduced, and spam filtering was found to be a useful technique for ad hoc web search, unlike in past test collections.

The test collections developed at TREC are useful not just for (potentially) helping researchers advance the state of the art, but also for allowing developers of new (commercial) retrieval products to evaluate their effectiveness on standard tests. In the past decade, TREC has created new tests for enterprise e-mail search, genomics search, spam filtering, e-Discovery, and several other retrieval domains.

TREC systems often provide a baseline for further research. Examples include:

• Hal Varian, Chief Economist at Google, says Better data makes for better science. The history of information retrieval illustrates this principle well," and describes TREC's contribution.[11]
• TREC's Legal track has influenced the e-Discovery community both in research and in evaluation of commercial vendors.[12]
• The IBM researcher team building IBM Watson (aka DeepQA), which beat the world's best Jeopardy! players,[13] used data and systems from TREC's QA Track as baseline performance measurements.[14]

The conference is made up of a varied, international group of researchers and developers.[15][16][17] In 2003, there were 93 groups from both academia and industry from 22 countries participating.

• List of computer science awards

• TREC website at NIST
• TIPSTER
• The TREC book (at Amazon)