1. Information Retrieval

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      Discourse, Entailment, Machine Translation, NER, Parsing, Segmentation, Semantic, Sentiment, Summarization, WSD
  2. About Information Retrieval

    Information retrieval (IR) is the science of searching for information in documents, searching for documents themselves, searching for metadata which describe documents, or searching within databases, whether relational stand-alone databases or hypertextually-networked databases such as the World Wide Web. There is a common confusion, however, between data retrieval, document retrieval, information retrieval, and text retrieval, and each of these has its own bodies of literature, theory, praxis and technologies. IR is, like most nascent fields, interdisciplinary, based on computer science, mathematics, library science, information science, cognitive psychology, linguistics, statistics, physics.

    Automated IR systems are used to reduce information overload. Many universities and public libraries use IR systems to provide access to books, journals, and other documents. IR systems are often related to object and query. Queries are formal statements of information needs that are put to an IR system by the user. An object is an entity which keeps or stores information in a database. User queries are matched to objects stored in the database. A document is, therefore, a data object. Often the documents themselves are not kept or stored directly in the IR system, but are instead represented in the system by document surrogates.

    In 1992 the US Department of Defense, along with the National Institute of Standards and Technology (NIST), cosponsored the Text Retrieval Conference (TREC) as part of the TIPSTER text program. The aim of this was to look into the information retrieval community by supplying the infrastructure that was needed for such a huge evaluation of text retrieval methodologies.

    Web search engines such as Google, Yahoo search, or Live.com are the most visible IR applications."

  3. Quotes about Information Retrieval

    1. The NIST evaluation further validates our hybrid approach to machine translation as being the most efficient and useful in providing accurate and informative multi-lingual information retrieval.
      Hassan Sawaf in AppTek Scores Highest in 2009 NIST Testing of Machine Translation ...
    2. The benefits of the enhanced ASR engine go beyond traditional uses, as we are able to provide customers the industry's first solution to employ Spanish transcription and translation in one platform, which can be utilized for many leading-edge machine translation and multilingual information retrieval projects, including media monitoring where it adjusts to changes in dialect and language in real-time to improve speed and accuracy.
      Hassan Sawaf in Bridging the Language Gap: AppTek Announces Major Enhancement of Automated ...
    3. This type of problem is more common in Swedish than in English since compound words are rare in English compared to in Swedish. The fact that almost all information retrieval research has focused on English, a language with entirely different inherent problems, suggests that more Swedish research in the area is essential.
      Karin Friberg Heppin in Effective search terms yield the right information