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Progress in Machines and Systems

Deriving from Pre-trained Word Embedding
Akanksha Mishra, Sukomal Pal
Indian Institute of Technology (BHU), Varanasi - 221005 & India
Abstract: Question answering and classification is analysed from different angles by researchers. Organising and classifying questions in the query formulation is addressed using different techniques where the word embedding is one. IN this paper we present our work related to using pre trained GloVe word embedding. Besides, we processed the results with different other embedding.
Keywords: Quora Insincere Question, Bidirectional Long Short Term Memory, GloVe Embedding Deriving from Pre-trained Word Embedding
DOI:https://doi.org/10.6025/pms/2020/9/2/52-56
Full_Text   PDF 291 KB   Download:   261  times
References:

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[2] Mikolov, T., Chen, K., Corrado, G., Dean, J. (2013). Efficient estimation of word representations in vector space.
[3] Pennington, J., Socher, R., Manning, C. D. (2014). Glove: Global vectors for word representation. In: Empirical Methods in Natural Language Processing (EMNLP). p. 1532–1543, http://www.aclweb.org/anthology/D14-1162
[4] Schuster, M., Paliwal, K. (1997). Bidirectional recurrent neural networks. Trans. Sig. Proc. 45 (11) 2673–2681 (November).https://doi.org/10.1109/78.650093, http://dx.doi.org/10.1109/78.650093
[5] Wieting, J., Bansal, M., Gimpel, K., Livescu, K. (2015). From paraphrase database to compositional paraphrase model and back. Transactions of the Association for Computational Linguistics 3, 345–358.


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