Skip to main content
SpringerLink
Log in
Book cover

International Conference on Multi-disciplinary Trends in Artificial Intelligence

MIWAI 2019: Multi-disciplinary Trends in Artificial Intelligence pp 3–14Cite as

Text Relation Extraction Using Sentence-Relation Semantic Similarity

  • Conference paper
  • First Online:

  • 760 Accesses

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11909))

Abstract

There is a huge amount of available information stored in unstructured plain text. Relation Extraction (RE) is an important task in the process of converting unstructured resources into machine-readable format. RE is usually considered as a classification problem where a set of features are extracted from the training sentences and thereafter passed to a classifier to predict the relation labels. Existing methods either manually design these features or automatically build them by means of deep neural networks. However, in many cases these features are general and do not accurately reflect the properties of the input sentences. In addition, these features are only built for the input sentences with no regard to the features of the target relations. In this paper, we follow a different approach to perform the RE task. We propose an extended autoencoder model to automatically build vector representations for sentences and relations from their distinctive features. The built vectors are high abstract continuous vector representations (embeddings) where task related features are preserved and noisy irrelevant features are eliminated. Similarity measures are then used to find the sentence-relation semantic similarities using their representations in order to label sentences with the most similar relations. The conducted experiments show that the proposed model is effective in labeling new sentences with their correct semantic relations.

This is a preview of subscription content, 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

Learn about institutional subscriptions

Notes

  1. 1.

    TransE provides continuous vector representations for entities and their relations in a knowledge graph.

  2. 2.

    Autoencoders that produce representations smaller that the input vectors are called undercomplete autoencoders, which are used to learn meaningful features and prevent copying the input vectors.

  3. 3.

    This is a common problem in gradient descent optimisation where the error minimisation stops before reaching the global optimised solution.

  4. 4.

    For better generalisation from the training to the testing sentences, the threshold may be reduced by a small fixed value for all the semantic relations.

References

  1. Lubani, M., Noah, S.A.M., Mahmud, R.: Ontology population: approaches and design aspects. J. Inf. Sci. 45(4), 502–515 (2019)

    Article  Google Scholar 

  2. Hazrina, S., Sharef, N.M., Ibrahim, H., Murad, M.A.A., Noah, S.A.M.: Review on the advancements of disambiguation in semantic question answering system. Inf. Process. Manage. 53(1), 52–69 (2017)

    Article  Google Scholar 

  3. Nasution, M.K., Noah, S.A.: Information retrieval model: a social network extraction perspective. In International Conference on Information Retrieval & Knowledge Management, pp. 322–326, IEEE, Kuala Lumpur (2012)

    Google Scholar 

  4. Brin, S.: Extracting patterns and relations from the world wide web. In: The World Wide Web and Databases, pp. 172–183 (1998)

    Google Scholar 

  5. Agichtein, E., Gravano, L.: Snowball: extracting relations from large plain-text collections. In: Proceedings of the Fifth ACM Conference on Digital Libraries, pp. 85–94. ACM (2000)

    Google Scholar 

  6. Ravichandran, D., Hovy, E.: Learning surface text patterns for a question answering system. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics (ACL), Philadelphia, pp. 41–47 (2002)

    Google Scholar 

  7. Alfonseca, E., Ruiz-Casado, M., Okumura, M., Castells, P.: Towards large-scale non-taxonomic relation extraction: estimating the precision of rote extractors. In: The 2nd Workshop on Ontology Learning and Population, Sydney, Australia, pp. 49–56 (2006)

    Google Scholar 

  8. Alfonseca, E., Castells, P., Okumura, M., Ruiz-Casado, M.: A rote extractor with edit distance-based generalisation and multi-corpora precision calculation. In: Proceedings of the COLING/ACL on Main Conference Poster Sessions, pp. 9–16. Association for Computational Linguistics (2006)

    Google Scholar 

  9. Etzioni, O., et al.: Unsupervised named-entity extraction from the web: an experimental study. Artif. Intell. 165, 91–134 (2005)

    Article  Google Scholar 

  10. GuoDong, Z., Jian, S., Jie, Z., Min, Z.: Exploring various knowledge in relation extraction. In: Proceedings of the 43rd Annual Meeting of the ACL, pp. 427–434. Association for Computational Linguistics, Ann Arbor (2005)

    Google Scholar 

  11. Chen, Y., Li, W., Liu, Y., Zheng, D., Zhao, T.: Exploring deep belief network for Chinese relation extraction. In: Proceedings of the CIPS-SIGHAN Joint Conference on Chinese Language Processing (CLP 2010), Beijing, China (2010)

    Google Scholar 

  12. Liu, C., Sun, W., Chao, W., Che, W.: Convolution neural network for relation extraction. In: Motoda, H., Wu, Z., Cao, L., Zaiane, O., Yao, M., Wang, W. (eds.) ADMA 2013. LNCS (LNAI), vol. 8347, pp. 231–242. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-53917-6_21

    Chapter  Google Scholar 

  13. Zeng, D., Liu, K., Lai, S., Zhou, G., Zhao, J.: Relation classification via convolutional deep neural network. In: International Conference on Computational Linguistics, Dublin, Ireland, pp. 2335–2344 (2014)

    Google Scholar 

  14. Suchanek, F.M., Ifrim, G., Weikum, G.: LEILA: learning to extract information by linguistic analysis. In: Proceedings of the 2nd Workshop on Ontology Learning and Population: Bridging the Gap between Text and Knowledge, Sydney, Australia, pp. 18–25 (2006)

    Google Scholar 

  15. Zelenko, D., Aone, C., Richardella, A.: Kernel methods for relation extraction. J. Mach. Learn. Res. 3, 1083–1106 (2003)

    MathSciNet  MATH  Google Scholar 

  16. Culotta, A., Sorensen, J.: Dependency tree kernels for relation extraction. In: ACL 2004 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics, p. 423. Association for Computational Linguistics, Barcelona (2004)

    Google Scholar 

  17. Bunescu, R., Mooney, R.: A shortest path dependency kernel for relation extraction. In: Proceedings of Human Language Technology Conference and Conference on Empirical Methods in Natural Language Processing, pp. 724–731. Association for Computational Linguistic, Vancouver (2005)

    Google Scholar 

  18. Zhou, G., Zhang, M., Ji, D.H., Zhu, Q.: Tree kernel-based relation extraction with context-sensitive structured parse tree information. In: Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP-CoNLL), pp. 728–736. Association for Computational Linguistics, Prague (2007)

    Google Scholar 

  19. Zhang, M., Zhang, J., Su, J., Zhou, G.: A composite kernel to extract relations between entities with both flat and structured features. In: Proceedings of the 21st International Conference on Computational Linguistics and the 44th Annual Meeting of the Association for Computational Linguistics, pp. 825–832. Association for Computational Linguistics, Sydney, Australia (2006)

    Google Scholar 

  20. Bunescu, R.C., Mooney, R.J.: Subsequence kernels for relation extraction. In: NIPS 2005 Proceedings of the 18th International Conference on Neural Information Processing Systems, pp. 171–178. MIT Press, Cambridge (2005)

    Google Scholar 

  21. Mintz, M., Bills, S., Snow, R., Jurafsky, D.: Distant supervision for relation extraction without labeled data. In: Proceedings of the 47th Annual Meeting of the ACL and the 4th IJCNLP of the AFNLP, Suntec, Singapore, pp. 1003–1011 (2009)

    Google Scholar 

  22. Su, Y., Liu, H., Yavuz, S., Gür, I., Sun, H., Yan, X.: Global relation embedding for relation extraction. In: Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers), pp. 820–830. Association for Computational Linguistics, New Orleans (2018)

    Google Scholar 

  23. Zeng, D., Liu, K., Chen, Y., Zhao, J.: Distant supervision for relation extraction via piecewise convolutional neural networks. In: Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, pp. 1753–1762. Association for Computational Linguistics, Lisbon (2015)

    Google Scholar 

  24. Liu, Y., Li, S., Wei, F., Ji, H.: Relation classification via modeling augmented dependency paths. IEEE/ACM Trans. Audio Speech Lang. Process. 24(9), 1589–1598 (2016)

    Article  Google Scholar 

  25. Bordes, A., Usunier, N., Garcia-Durán, A., Weston, J., Yakhnenko, O.: Translating embeddings for modeling multi-relational data. In: NIPS 2013 Proceedings of the 26th International Conference on Neural Information Processing Systems, vol. 2, pp. 2787–2795. Curran Associates Inc., Lake Tahoe (2013)

    Google Scholar 

  26. Wang, G., et al.: Label-free distant supervision for relation extraction via knowledge graph embedding. In: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pp. 2246–2255. Association for Computational Linguistics, Brussels (2018)

    Google Scholar 

  27. Rumelhart, D.E., McClelland, J.L., Asanuma, C.: Learning internal representations by error propagation. In: Parallel Distributed Processing: Explorations in the Microstructure of Cognition, pp. 318–362. MIT Press Cambridge (1986)

    Google Scholar 

  28. Mikolov, T., Sutskever, I., Chen, K., Corrado, G., Dean, J.: Distributed representations of words and phrases and their compositionality. In: Advances in Neural Information Processing Systems, pp. 3111–3119. Curran Associates Inc., USA (2013)

    Google Scholar 

  29. Pennington, J., Socher, R., Manning, C.: GloVe: global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543. Association for Computational Linguistics, Doha (2014)

    Google Scholar 

  30. Abadi, M., et al.: Tensorflow: Large-scale machine learning on heterogeneous distributed systems. arXiv preprint arXiv:1603.04467 (2016)

  31. Goldberg, Y., Nivre, J.: A dynamic Oracle for arc-eager dependency parsing. In: Proceedings of COLING 2012, pp. 959–976. The COLING 2012 Organizing Committee, Mumbai, India (2012)

    Google Scholar 

  32. Girju, R., Nakov, P., Nastase, V., Szpakowicz, S., Turney, P., Yuret, D.: SemEval-2007 task 04: classification of semantic relations between nominals. In: SemEval 2007 Proceedings of the 4th International Workshop on Semantic Evaluations, pp. 13–18. Association for Computational Linguistics, Prague (2007)

    Google Scholar 

  33. Noah, S.A., Omar, N., Amruddin, A.Y.: Evaluation of lexical-based approaches to the semantic similarity of Malay sentences. J. Quantit. Ling. 22(2), 135–156 (2015)

    Article  Google Scholar 

  34. Noah, S.A., Amruddin, A.Y., Omar, N.: Semantic Similarity Measures for Malay Sentences. In: Goh, D.H.-L., Cao, T.H., Sølvberg, I.T., Rasmussen, E. (eds.) ICADL 2007. LNCS, vol. 4822, pp. 117–126. Springer, Heidelberg (2007). https://doi.org/10.1007/978-3-540-77094-7_19

    Chapter  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Center for Artificial Intelligence Technology, Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia, 43600, Bangi, Selangor, Malaysia

    Mohamed Lubani & Shahrul Azman Mohd Noah

Authors
  1. Mohamed Lubani
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Shahrul Azman Mohd Noah
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mohamed Lubani .

Editor information

Editors and Affiliations

  1. Mahasarakham University, Maha Sarakham, Thailand

    Rapeeporn Chamchong

  2. Murdoch University, Murdoch, WA, Australia

    Kok Wai Wong

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Lubani, M., Noah, S.A.M. (2019). Text Relation Extraction Using Sentence-Relation Semantic Similarity. In: Chamchong, R., Wong, K. (eds) Multi-disciplinary Trends in Artificial Intelligence. MIWAI 2019. Lecture Notes in Computer Science(), vol 11909. Springer, Cham. https://doi.org/10.1007/978-3-030-33709-4_1

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-33709-4_1

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-33708-7

  • Online ISBN: 978-3-030-33709-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation