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      Word Embedding based Approaches for Information Retrieval

      proceedings-article
      Seventh BCS-IRSG Symposium on Future Directions in Information Access (FDIA)
      Future Directions in Information Access
      5 September 2017
      Word Embedding, Information Retrieval
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            Abstract

            The interest on using word embedding has expanded in various areas of text processing in recent years following the introduction of the word2vec model by Mikolov et al. (2013) and Pennington et al. (2014). The word embedding models use a large amount of text to create low dimensional representations of words capturing relationships between words without any external supervision. The resultant representation is shown to replicate many linguistic regularities such as, semantic similarity between terms, conceptual composition of terms, laws of analogy of terms. These features can be used for Information Retrieval (IR) where, the retrieval functions primarily depend on statistical co-occurrences.

            Content

            Author and article information

            Contributors
            Conference
            September 2017
            September 2017
            : 1-4
            Affiliations
            [0001]Indian Statistical Institute, Kolkata

            203, B.T. Road

            Kolkata, India - 700108
            Article
            10.14236/ewic/FDIA2017.9
            8af0836d-9c99-4bc7-a184-6f9c940af686
            © Roy. Published by BCS Learning and Development Ltd. Proceedings of the 7th Symposium on Future Directions in Information Access 2017

            This work is licensed under a Creative Commons Attribution 4.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

            Seventh BCS-IRSG Symposium on Future Directions in Information Access
            FDIA
            7
            Barcelona, Spain
            5 September 2017
            Electronic Workshops in Computing (eWiC)
            Future Directions in Information Access
            Product
            Product Information: 1477-9358BCS Learning & Development
            Self URI (article page): https://www.scienceopen.com/hosted-document?doi=10.14236/ewic/FDIA2017.9
            Self URI (journal page): https://ewic.bcs.org/
            Categories
            Electronic Workshops in Computing

            Applied computer science,Computer science,Security & Cryptology,Graphics & Multimedia design,General computer science,Human-computer-interaction
            Word Embedding,Information Retrieval

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