Text REtrieval Conference (TREC)

The Text REtrieval Conference (TREC) is an ongoing series of workshops focusing on a list of different information retrieval (IR) research areas, or ''tracks.'' It is co-sponsored by the National Institute of Standards and Technology (NIST) and the Intelligence Advanced Research Projects Activity (part of the office of the Director of National Intelligence), and began in 1992 as part of the TIPSTER Text program. Its purpose is to support and encourage research within the information retrieval community by providing the infrastructure necessary for large-scale ''evaluation'' of text retrieval methodologies and to increase the speed of lab-to-product transfer of technology.

TREC's evaluation protocols have improved many search technologies. A 2010 study estimated that "without TREC, U.S. Internet users would have spent up to 3.15 billion additional hours using web search engines between 1999 and 2009." Hal Varian the Chief Economist at Google wrote that "The TREC data revitalized research on information retrieval. Having a standard, widely available, and carefully constructed set of data laid the groundwork for further innovation in this field."

Each track has a challenge wherein NIST provides participating groups with data sets and test problems. Depending on track, test problems might be questions, topics, or target extractable features. Uniform scoring is performed so the systems can be fairly evaluated. After evaluation of the results, a workshop provides a place for participants to collect together thoughts and ideas and present current and future research work.Text Retrieval Conference started in 1992, funded by DARPA (US Defense Advanced Research Project) and run by NIST. Its purpose was to support research within the information retrieval community by providing the infrastructure necessary for large-scale evaluation of text retrieval methodologies.

Goals

* 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.

Relevance judgments in TREC

TREC uses binary relevance criterion that is either the document is relevant or not relevant. Since size of TREC collection is large, it is impossible to calculate the absolute recall for each query. In order to assess the relevance of documents in relation to a query, TREC uses a specific method call pooling for calculating relative recall. All the relevant documents that occurred in the top 100 documents for each system and for each query are combined to produce a pool of relevant documents. Recall being the proportion of the pool of relevant documents that a single system retrieved for a query topic.

Various TRECs

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)

Tracks

Current tracks

''New tracks are added as new research needs are identified, this list is current for TREC 2018.''

CENTRE Track
- Goal: run in parallel CLEF 2018, NTCIR-14, TREC 2018 to develop and tune an IR reproducibility evaluation protocol (new track for 2018).

Common Core Track
- Goal: an ad hoc search task over news documents.

Complex Answer Retrieval (CAR)
- Goal: to develop systems capable of answering complex information needs by collating information from an entire corpus.

Incident Streams Track
- Goal: to research technologies to automatically process social media streams during emergency situations (new track for TREC 2018).

The News Track
- Goal: partnership with The Washington Post to develop test collections in news environment (new for 2018).

Precision Medicine Track
- Goal: a specialization of the Clinical Decision Support track to focus on linking oncology patient data to clinical trials.

Real-Time Summarization Track (RTS)
- Goal: to explore techniques for real-time update summaries from social media streams.

Past tracks

* Chemical Track - Goal: to develop and evaluate technology for large scale search in chemistry-related documents, including academic papers and patents, to better meet the needs of professional searchers, and specifically patent searchers and chemists.

Clinical Decision Support Track
- Goal: to investigate techniques for linking medical cases to information relevant for patient care

Contextual Suggestion Track
- Goal: to investigate search techniques for complex information needs that are highly dependent on context and user interests.
* Crowdsourcing Track - Goal: to provide a collaborative venue for exploring crowdsourcing methods both for evaluating search and for performing search tasks.
* Genomics Track - Goal: to study the retrieval of genomic data, not just gene sequences but also supporting documentation such as research papers, lab reports, etc. Last ran on TREC 2007.

Dynamic Domain Track
- Goal: to investigate domain-specific search algorithms that adapt to the dynamic information needs of professional users as they explore in complex domains.
* Enterprise Track - Goal: to study search over the data of an organization to complete some task. Last ran on TREC 2008.
* Entity Track - Goal: to perform entity-related search on Web data. These search tasks (such as finding entities and properties of entities) address common information needs that are not that well modeled as ad hoc document search.
* Cross-Language Track - Goal: to investigate the ability of retrieval systems to find documents topically regardless of source language. After 1999, this track spun off into CLEF.
* FedWeb Track - Goal: to select best resources to forward a query to, and merge the results so that most relevant are on the top.
* Federated Web Search Track - Goal: to investigate techniques for the selection and combination of search results from a large number of real on-line web search services.
* Filtering Track - Goal: to binarily decide retrieval of new incoming documents given a stable information need.
* HARD Track - Goal: to achieve High Accuracy Retrieval from Documents by leveraging additional information about the searcher and/or the search context.
* Interactive Track - Goal: to study user interaction with text retrieval systems.

Knowledge Base Acceleration (KBA)
Track - Goal: to develop techniques to dramatically improve the efficiency of (human) knowledge base curators by having the system suggest modifications/extensions to the KB based on its monitoring of the data streams, created th

organized by Diffeo.
* Legal Track - Goal: to develop search technology that meets the needs of lawyers to engage in effective discovery in digital document collections.

LiveQA Track
- Goal: to generate answers to real questions originating from real users via a live question stream, in real time.
* Medical Records Track - Goal: to explore methods for searching unstructured information found in patient medical records.
* Microblog