1. John , B. , Ryan , M. , & Fernando , P. ( 2006 ) Domain adaptation with structural correspondence learning , Empirical Methods in Natural Language Processing, W06-16.: 120-128. John, B., Ryan, M., & Fernando, P. (2006) Domain adaptation with structural correspondence learning, Empirical Methods in Natural Language Processing, W06-16.: 120-128.
2. Mikolov , T. , Sutskever , I. , Chen , K. , Corrado , G. S. , & Dean , J. ( 2013 ). Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems, 26 . Mikolov, T., Sutskever, I., Chen, K., Corrado, G. S., & Dean, J. (2013). Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems, 26.
3. Glove: Global Vectors for Word Representation
4. Dai , A. M. , & Le , Q. V. ( 2015 ). Semi-supervised sequence learning. Advances in neural information processing systems, 28 . Dai, A. M., & Le, Q. V. (2015). Semi-supervised sequence learning. Advances in neural information processing systems, 28.
5. Kiros , R. , Zhu , Y. , Salakhutdinov , R. R. , Zemel , R. , Urtasun , R. , Torralba , A. , & Fidler , S. ( 2015 ). Skip-thought vectors. Advances in neural information processing systems, 28 . Kiros, R., Zhu, Y., Salakhutdinov, R. R., Zemel, R., Urtasun, R., Torralba, A., & Fidler, S. (2015). Skip-thought vectors. Advances in neural information processing systems, 28.