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  2. Question answering - Wikipedia

    en.wikipedia.org/wiki/Question_answering

    In information retrieval, an open-domain question answering system tries to return an answer in response to the user's question. The returned answer is in the form of short texts rather than a list of relevant documents. [13] The system finds answers by using a combination of techniques from computational linguistics, information retrieval, and ...

  3. List of datasets for machine-learning research - Wikipedia

    en.wikipedia.org/wiki/List_of_datasets_for...

    An end-to-end open-domain question answering. This dataset includes 14,000 conversations with 81,000 question-answer pairs. Context, Question, Rewrite, Answer, Answer_URL, Conversation_no, Turn_no, Conversation_source Further details are provided in the project's GitHub repository and respective Hugging Face dataset card. Question Answering ...

  4. List of datasets in computer vision and image processing

    en.wikipedia.org/wiki/List_of_datasets_in...

    Large dataset of images for object classification. Images categorized and hand-sorted. 30,607 Images, Text Classification, object detection 2007 [29] [30] G. Griffin et al. COYO-700M Image–text-pair dataset 10 billion pairs of alt-text and image sources in HTML documents in CommonCrawl 746,972,269 Images, Text Classification, Image-Language ...

  5. Winograd schema challenge - Wikipedia

    en.wikipedia.org/wiki/Winograd_schema_challenge

    The Winograd schema challenge (WSC) is a test of machine intelligence proposed in 2012 by Hector Levesque, a computer scientist at the University of Toronto.Designed to be an improvement on the Turing test, it is a multiple-choice test that employs questions of a very specific structure: they are instances of what are called Winograd schemas, named after Terry Winograd, professor of computer ...

  6. Training, validation, and test data sets - Wikipedia

    en.wikipedia.org/wiki/Training,_validation,_and...

    A training data set is a data set of examples used during the learning process and is used to fit the parameters (e.g., weights) of, for example, a classifier. [9] [10]For classification tasks, a supervised learning algorithm looks at the training data set to determine, or learn, the optimal combinations of variables that will generate a good predictive model. [11]

  7. Legal expert system - Wikipedia

    en.wikipedia.org/wiki/Legal_expert_system

    A legal expert system is a domain-specific expert system that uses artificial intelligence to emulate the decision-making abilities of a human expert in the field of law. [1]: 172 Legal expert systems employ a rule base or knowledge base and an inference engine to accumulate, reference and produce expert knowledge on specific subjects within the legal domain.

  8. Question and answer system - Wikipedia

    en.wikipedia.org/wiki/Question_and_answer_system

    A question and answer system (or Q&A system) is an online software system that attempts to answer questions asked by users.Q&A software is frequently integrated by large and specialist corporations and tends to be implemented as a community that allows users in similar fields to discuss questions and provide answers to common and specialist questions.

  9. Legal information retrieval - Wikipedia

    en.wikipedia.org/wiki/Legal_information_retrieval

    Accurate legal information retrieval is important to provide access to the law to laymen and legal professionals. Its importance has increased because of the vast and quickly increasing amount of legal documents available through electronic means. [2] Legal information retrieval is a part of the growing field of legal informatics.