Refined stop-words and morphological variants solutions applied to Hindi-English cross-lingual information retrieval

Author:

Sharma Vijay1,Mittal Namita1

Affiliation:

1. Department of Computer Science & Engineering, Malaviya National Institute of Technology Jaipur, India

Publisher

IOS Press

Subject

Artificial Intelligence,General Engineering,Statistics and Probability

Reference31 articles.

1. S.S. Akhtar , A. Gupta , A. Vajpayee , A. Srivastava and M. Shrivastava , Unsupervised morphological expansion of small datasets for improving word embeddings. arXiv preprint arXiv:1711.05678, 2017.

2. D. Bahdanau , K. Cho and Y. Bengio , Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv:1409.0473., 2014.

3. O. Bojar , V. Diatka , P. Rychly , P. Stranak , V. Suchomel , A. Tamchyna and D. Zeman , HindEnCorp-Hindi-English and hindi-only corpus for machine translation, In LREC, 2014, pp. 3550–3555.

4. Convolutional over recurrent encoder for neural machine translation;Dakwale;The Prague Bulletin of Mathematical Linguistics,2017

5. J. Du , H. Hou , J. Wu , Z. Shen , J. Li and H. Wang , Key Research of Preprocessing on Mongolian-Chinese Neural Machine Translation, 2016.

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