Skip to main content
SpringerLink
Log in

Matrix Models with Feature Enrichment for Relation Extraction

  • Conference paper
  • First Online:
Advances in Artificial Intelligence (Canadian AI 2017)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10233))

Included in the following conference series:

  • 1818 Accesses

Abstract

Many traditional relation extraction techniques require a large number of pre-defined schemas in order to extract relations from textual documents. In this paper, to avoid the need for pre-defined schemas, we employ the notion of universal schemas that is formed as a collection of patterns derived from Open Information Extraction as well as from relation schemas of pre-existing datasets. We then employ matrix factorization and collaborative filtering on such universal schemas for relation extraction. While previous systems have trained relations only for entities, we exploit advanced features from relation characteristics such as clause types and semantic topics for predicting new relation instances. This helps our proposed work to naturally predict any tuple of entities and relations regardless of whether they were seen at training time with direct or indirect access in their provenance. In our experiments, we show improved performance compared to the state-of-the-art.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    http://nlp.stanford.edu/software/mimlre-2014-07-17-data.tar.gz.

  2. 2.

    http://gibbslda.sourceforge.net.

References

  1. Angeli, G., Tibshirani, J., Wu, J., Manning, C.D.: Combining distant and partial supervision for relation extraction. In: EMNLP 2014 (2014)

    Google Scholar 

  2. Blei, D., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. J. Mach. Learn. Res. 3, 993–1022 (2003)

    MATH  Google Scholar 

  3. Bollegala, D., Matsuo, Y., Ishizuka, Y.: Relational duality: unsupervised extraction of semantic relations between entities on the web. In: WWW 2010 (2010)

    Google Scholar 

  4. Collins, M., Dasgupta, S., Schapire, R.S.: A generalization of principal component analysis to the exponential family. In: NIPS 2001 (2001)

    Google Scholar 

  5. Corro, L.D., Gemulla, R.: ClausIE: clause-based open information extraction. In: WWW 2013 (2013)

    Google Scholar 

  6. Fader, A., Soderland, S., Etzioni, O.: Identifying relations for open information extraction. In: EMNLP 2011 (2011)

    Google Scholar 

  7. Greenwood, M.A., Stevenson, M.: Improving semi-supervised acquisition of relation extraction patterns. In: IEBD 2006 (2006)

    Google Scholar 

  8. Kambhatla, N.: Combining lexical, syntactic and semantic features with maximum entropy models for extracting relations. In: ACL 2004 (2004)

    Google Scholar 

  9. Kemp, C., Tenenbaum, J.B., Griffiths, T.L.: Learning systems of concepts with an infinite relational model. In: AAAI 2006 (2006)

    Google Scholar 

  10. Koren, Y.: Factorization meets the neighborhood: a multifaceted collaborative filtering model. In: KDD 2009 (2009)

    Google Scholar 

  11. Mausam, Schmitz, M., Bart, R., Soderland, S., Etzioni, O.: Open language learning for information extraction. In: EMNLP 2012 (2012)

    Google Scholar 

  12. Pantel, P., Pennacchiotti, M.: Espresso: leveraging generic patterns for automatically harvesting semantic relations. In: COLING 2006 (2006)

    Google Scholar 

  13. Phan, X.H., Nguyen, C.T., Le, D.T., Nguyen, L.M., Horiguchi, S., Ha, Q.T.: A hidden topic-based framework toward building applications with short web documents. IEEE Trans. Knowl. Data Eng. 23, 961–976 (2011)

    Article  Google Scholar 

  14. Rendle, S., Freudenthaler, C., Gantner, Z., Schmidt-Thieme, L.: Bayesian personalized ranking from implicit feedback. In: Proceedings of UAI 2009 (2009)

    Google Scholar 

  15. Riedel, S., Yao, L., McCallum, A., Marlin, M.: Relation extraction with matrix factorization and universal schemas. In: NAACL 2013 (2013)

    Google Scholar 

  16. Surdeanu, M., Tibshirani, J., Nallapati, R., Manning, C.D.: Multi-instance multi-label learning for relation extraction. In: EMNLP-CoNLL 2012 (2012)

    Google Scholar 

  17. Takamatsu, S., Sato, I., Nakagawa, H.: Probabilistic matrix factorization leveraging contexts for unsupervised relation discovery. In: PAKDD 2011 (2011)

    Google Scholar 

  18. Vo, D.T., Bagheri, E.: Self-training on refined clause patterns for relation extraction. Inf. Process. Manage. (2017). doi:10.1016/j.ipm.2017.02.009

  19. Vo, D.T., Bagheri, E.: Open information extraction. Encycl. Semant. Comput. Robot. Intell. 1(1) (2017). doi:10.1142/S2425038416300032

  20. Wu, F., Weld, D.S.: Open information extraction using wikipedia. In: ACL 2010

    Google Scholar 

  21. Zhou, G., Qian, L., Fan, J.: Tree kernel based semantic relation extraction with rich syntactic and semantic information. Inf. Sci. 180, 1313–1325 (2010)

    Article  MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Laboratory for Systems, Software and Semantics (LS3), Ryerson University, Toronto, Canada

    Duc-Thuan Vo & Ebrahim Bagheri

Authors
  1. Duc-Thuan Vo
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ebrahim Bagheri
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Duc-Thuan Vo .

Editor information

Editors and Affiliations

  1. University of Regina, Regina, Saskatchewan, Canada

    Malek Mouhoub

  2. University of Montreal, Montreal, Québec, Canada

    Philippe Langlais

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing AG

About this paper

Cite this paper

Vo, DT., Bagheri, E. (2017). Matrix Models with Feature Enrichment for Relation Extraction. In: Mouhoub, M., Langlais, P. (eds) Advances in Artificial Intelligence. Canadian AI 2017. Lecture Notes in Computer Science(), vol 10233. Springer, Cham. https://doi.org/10.1007/978-3-319-57351-9_28

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-57351-9_28

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-57350-2

  • Online ISBN: 978-3-319-57351-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation