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@article{154322,
        author = {D.S.Sameena Begum},
        title = {Context Based Information extraction based on a query using deep learning model},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {8},
        number = {10},
        pages = {540-550},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=154322},
        abstract = {Question Answering systems (QA)  technology that provides the answer, rather than a list of possible answers, can be referred to as an algorithm. Text similarity and questions asked in natural language are the primary concerns for QA systems in this scenario. Several deep learning models for answering questions have been proposed. Local maxima corresponding to incorrect answers cannot be recovered because of their single-pass nature. So, we're going to use an algorithm called the Dynamic Coattention Network (DCN) to answer questions. DCN combines question and document representations that focus on the most relevant parts of each. Decoders iterate over possible answer spans using dynamic pointing. An initial local maxima associated with incorrect answers can be recovered using this iterative procedure. For Stanford question answering, this DCN ensemble model scores 80.4 percent accuracy.},
        keywords = {Natural Language Processing, deep learning model, Dynamic Coattention networks, bidirectional LSTM.},
        month = {},
        }