Advertisement

NLP and Large-Scale Information Retrieval on Mathematical Texts

  • Yihe DongEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10931)

Abstract

We present a recommender system covering math and math physics papers from the arXiv, to assist researchers to quickly retrieve theorems and discover similar results from this vast corpus. The retrieval aims to discover not just syntactic, but also semantic similarity. We will discuss the challenges encountered and the experimental methodologies used.

Keywords

NLP Information retrieval 

Notes

Acknowledgments

We would like to thank Jeremy Michelson and Michael Trott for continuously lending their ears and ideas throughout this project, as well as Rob Y. Lewis and the ICMS reviewer for helpful comments on an earlier draft of this paper.

References

  1. 1.
  2. 2.
    Microsoft Academic. https://academic.microsoft.com
  3. 3.
    Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. https://arxiv.org/abs/1301.3781
  4. 4.
    arXiv Bulk Data Access. https://arxiv.org/help/bulk_data_s3
  5. 5.
    Universal Dependencies. http://universaldependencies.org/
  6. 6.
    Corpus of Contemporary American English. https://corpus.byu.edu/coca/
  7. 7.
    Word frequency data. https://www.wordfrequency.info/
  8. 8.
  9. 9.
    Furnas, G., Dumais, S., Landauer, T.K., Harshman, R.A., Streeter, L.A., Lochbaum, K.E.: Information retrieval using a singular value decomposition model of latent semantic structure. In: Proceedings of SIGIR (1998)Google Scholar
  10. 10.
    Toutanova, K., Klein, D., Manning, C., Singer, Y.: Feature-rich part-of-speech tagging with a cyclic dependency network. In: Proceedings of HLT-NAACL, pp. 252–259 (2003)Google Scholar
  11. 11.
    Kaliszyk, C., Urban, J., Vyskočil, J.: Automating formalization by statistical and semantic parsing of mathematics. In: Ayala-Rincón, M., Muñoz, C.A. (eds.) ITP 2017. LNCS, vol. 10499, pp. 12–27. Springer, Cham (2017).  https://doi.org/10.1007/978-3-319-66107-0_2CrossRefGoogle Scholar
  12. 12.
    Conneau, A., Kiela, D., Schwenk, H., Barrault, L., Bordes, A.: Supervised learning of universal sentence representations from natural language inference data. https://arxiv.org/abs/1705.02364
  13. 13.
    Arora, S., Liang, Y., Ma, T.: A simple but tough-to-beat baseline for sentence embeddings. In: ICLR (2017)Google Scholar
  14. 14.
    Pagliardini, M., Gupta, P., Jaggi, M.: Unsupervised learning of sentence embeddings using compositional n-gram features. https://arxiv.org/abs/1703.02507

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Wolfram ResearchChampaignUSA

Personalised recommendations