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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 ...
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 ...
The HAI researchers, who included a Stanford Law professor, created a dataset of 200 questions designed to mimic the kinds of questions a lawyer might ask a legal research copilot.
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 ...
A question answering task is considered "open book" if the model's prompt includes text from which the expected answer can be derived (for example, the previous question could be adjoined with some text which includes the sentence "The Sharks have advanced to the Stanley Cup finals once, losing to the Pittsburgh Penguins in 2016." [125]).
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]
a natural-language system working in restricted "blocks worlds" with restricted vocabularies, worked extremely well PARRY: 1972 Kenneth Colby: A chatterbot: KL-ONE: 1974 Sondheimer et al. a knowledge representation system in the tradition of semantic networks and frames; it is a frame language. MARGIE 1975 Roger Schank: TaleSpin (software) 1976 ...
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.