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  1. aclanthology.org

    %0 Conference Proceedings %T Character-based Neural Embeddings for Tweet Clustering %A Vakulenko, Svitlana %A Nixon, Lyndon %A Lupu, Mihai %Y Ku, Lun-Wei %Y Li, Cheng-Te %S Proceedings of the Fifth International Workshop on Natural Language Processing for Social Media %D 2017 %8 April %I Association for Computational Linguistics %C Valencia, Spain %F vakulenko-etal-2017-character %X In this ...
    Author:Svitlana Vakulenko, Lyndon J. B. Nixon, Mihai LupuPublished:2017
    • Character-based Neural Embeddings for Tweet Clustering - ACL Anthology

      The main advantage of the character-based ap-proaches is their language-independence, since they do not require any language-specic parsing. The major contribution of our work is the eval-uation of the character-based neural embeddings on the tweet clustering task. We show how to employ character-based tweet embeddings for the

  2. Abstract: In this paper we show how the performance of tweet clustering can be improved by leveraging character-based neural networks. The proposed approach overcomes the limitations related to the vocabulary explosion in the word-based models and allows for the seamless processing of the multilingual content.
    Author:Svitlana Vakulenko, Lyndon Nixon, Mihai LupuPublished:2017
  3. aclanthology.org

    The main advantage of the character-based ap-proaches is their language-independence, since they do not require any language-specic parsing. The major contribution of our work is the eval-uation of the character-based neural embeddings on the tweet clustering task. We show how to employ character-based tweet embeddings for the
  4. Title: Character-based Neural Embeddings for Tweet Clustering. Authors: Svitlana Vakulenko, Lyndon Nixon, Mihai Lupu (Submitted on 15 Mar 2017 , last revised 16 Mar 2017 (this version, v2)) Abstract: In this paper we show how the performance of tweet clustering can be improved by leveraging character-based neural networks. The proposed approach ...
    Author:Svitlana Vakulenko, Lyndon Nixon, Mihai LupuPublished:2017
  5. academia.edu

    The result is a matrix of size n × h, where n is the number of tweets and h is the number of hidden states (500). 3.3 Clustering To cluster tweet vectors (character-based tweet embeddings produced by the neural network for Tweet2Vec evaluation or the document-term matrix for TweetTerm) we employ the hierarchical clustering algorithm ...
  6. researchgate.net

    Twee2Vec [33] learns the vector-space representations of tweets using a character-based bi-directional recurrent neural network model, and has been demon- strated to have good performance in the ...
  7. semanticscholar.org

    This paper shows how the performance of tweet clustering can be improved by leveraging character-based neural networks and allows for the seamless processing of the multilingual content. In this paper we show how the performance of tweet clustering can be improved by leveraging character-based neural networks. The proposed approach overcomes the limitations related to the vocabulary explosion ...
  8. The main advantage of the character-based ap-proaches is their language-independence, since they do not require any language-specific parsing. The major contribution of our work is the eval-uation of the character-based neural embeddings on the tweet clustering task. We show how to employ character-based tweet embeddings for the
  9. In this paper we show how the performance of tweet clustering can be improved by leveraging character-based neural networks. The proposed approach overcomes... Skip to main content. We will keep fighting for all libraries - stand with us! A line drawing of the Internet Archive headquarters building façade. ...
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