Browsing by Subject "embedding of sentences"

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    Con-S2V: A Generic Framework for Incorporating Extra-Sentential Context into Sen2Vec
    (Springer, 2017) Saha, Tanay Kumar; Joty, Shafiq; Al Hasan, Mohammad; Computer and Information Science, School of Science
    We present a novel approach to learn distributed representation of sentences from unlabeled data by modeling both content and context of a sentence. The content model learns sentence representation by predicting its words. On the other hand, the context model comprises a neighbor prediction component and a regularizer to model distributional and proximity hypotheses, respectively. We propose an online algorithm to train the model components jointly. We evaluate the models in a setup, where contextual information is available. The experimental results on tasks involving classification, clustering, and ranking of sentences show that our model outperforms the best existing models by a wide margin across multiple datasets.