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Question answering (QA) is a computer science discipline within the fields of information retrieval and natural language processing (NLP) that is concerned with building systems that automatically answer questions that are posed by humans in a natural language.
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 ...
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.
Critiques and legal rulings [ edit ] Proponents of using AI and algorithms in the courtroom tend to argue that these solutions will mitigate predictable biases and errors in judges' reasoning, such as the hungry judge effect (the phenomenon that judges are more likely to make lenient decisions after eating a meal).
There is no single commonly accepted definition of a knowledge graph. Most definitions view the topic through a Semantic Web lens and include these features: [14] Flexible relations among knowledge in topical domains: A knowledge graph (i) defines abstract classes and relations of entities in a schema, (ii) mainly describes real world entities and their interrelations, organized in a graph ...
Short title: Department of Defense - Law of War Manual (June 2015) File change date and time: 07:09, 12 June 2015: Date and time of digitizing: 06:37, 12 June 2015
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]
This is a difficult task, as the legal field is prone to jargon, [5] polysemes [6] (words that have different meanings when used in a legal context), and constant change. Techniques used to achieve these goals generally fall into three categories: boolean retrieval, manual classification of legal text, and natural language processing of legal text.