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The high-level architecture of IBM's DeepQA used in Watson [9]. Watson was created as a question answering (QA) computing system that IBM built to apply advanced natural language processing, information retrieval, knowledge representation, automated reasoning, and machine learning technologies to the field of open domain question answering.
Question answering systems in the context of [vague] machine reading applications have also been constructed in the medical domain, for instance related to [vague] Alzheimer's disease. [3] Open-domain question answering deals with questions about nearly anything and can only rely on general ontologies and world knowledge. Systems designed for ...
The online subsystem answers questions submitted by users in real time. During the online process, TeLQAS processes the question using a natural language processing component that implements part-of-speech tagging and simple syntactic parsing. The online subsystem also utilizes an inference engine in order to carry out necessary inference on ...
New series of AI models are designed to help with complex tasks and harder problems, the company said OpenAI reveals new artificial intelligence tool it claims can think like a human before ...
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.
Its first product was an answer engine that aimed to directly answer questions on any subject posed in plain English text, which is accomplished using a database of discrete facts. [ 4 ] [ 5 ] The True Knowledge Answer engine was launched for private beta testing and development on 7 November 2007.
AI leaders are rethinking data-heavy training for large language models. Traditional models scale linearly with data, but this approach may hit a dead end. Smaller, more efficient models and new ...
NLU has been considered an AI-hard problem. [2] There is considerable commercial interest in the field because of its application to automated reasoning, [3] machine translation, [4] question answering, [5] news-gathering, text categorization, voice-activation, archiving, and large-scale content analysis.