Gregory Goth
Abstract
Neural net advances improve computers' language ability in many fields.
- Levy, O. and Goldberg, Y., Linguistic Regularities in Sparse and Explicit Word Representations. Proceedings of the 18th Conference on Computational Natural Language Learning, 2014. http://bit.ly/1OXBiciGoogle Scholar
Cross Ref
- Mikolov, T., Chen, K., Corrado, G., and Dean, J., Efficient Estimation of Word Representations in Vector Space. Proceedings of Workshop at International Conference on Learning Representations, 2013, http://arxiv.org/abs/1301.3781Google Scholar
- Goldberg, Y., and Levy, O., word2vec Explained: Deriving Mikolov et al.'s Negative-Sampling Word-Embedding Method, arXiv 2014. http://arxiv.org/abs/1402.3722Google Scholar
- Pennington, J., Socher, R., and Manning, C., GloVe: Global Vectors for Word Representation. Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing. http://nlp.stanford.edu/projects/glove/Google Scholar
- Moody, C. A Word is Worth a Thousand Vectors. MultiThreaded, StitchFix, 11 March 2015. http://bit.ly/1NL35xzGoogle Scholar
- Iyyer, M., Boyd-Graber, J., Claudino, L., Socher, R., and Daume III, H., A Neural Network For Factoid Question Answering Over Paragraphs. Proceedings of EMNLP 2014 https://cs.umd.edu/~miyyer/qblearn/Google Scholar
Index Terms
- Deep or shallow, NLP is breaking out
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