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QA4IE: A Question Answering Based Framework for Information Extraction

  • Lin Qiu
  • Hao Zhou
  • Yanru Qu
  • Weinan Zhang
  • Suoheng Li
  • Shu Rong
  • Dongyu Ru
  • Lihua Qian
  • Kewei Tu
  • Yong Yu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11136)

Abstract

Information Extraction (IE) refers to automatically extracting structured relation tuples from unstructured texts. Common IE solutions, including Relation Extraction (RE) and open IE systems, can hardly handle cross-sentence tuples, and are severely restricted by limited relation types as well as informal relation specifications (e.g., free-text based relation tuples). In order to overcome these weaknesses, we propose a novel IE framework named QA4IE, which leverages the flexible question answering (QA) approaches to produce high quality relation triples across sentences. Based on the framework, we develop a large IE benchmark with high quality human evaluation. This benchmark contains 293K documents, 2M golden relation triples, and 636 relation types. We compare our system with some IE baselines on our benchmark and the results show that our system achieves great improvements.

Notes

Acknowledgements

W. Zhang is the corresponding author of this paper. The work done by SJTU is sponsored by National Natural Science Foundation of China (61632017, 61702327, 61772333) and Shanghai Sailing Program (17YF1428200).

References

  1. 1.
    Abadi, M., et al.: Tensorflow: a system for large-scale machine learning. In: OSDI, vol. 16, pp. 265–283 (2016)Google Scholar
  2. 2.
    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
  3. 3.
    Angeli, G., Premkumar, M.J.J., Manning, C.D.: Leveraging linguistic structure for open domain information extraction. In: ACL, vol. 1, pp. 344–354 (2015)Google Scholar
  4. 4.
    Auer, S., Bizer, C., Kobilarov, G., Lehmann, J., Cyganiak, R., Ives, Z.: DBpedia: a nucleus for a web of open data. In: Aberer, K. (ed.) ASWC/ISWC -2007. LNCS, vol. 4825, pp. 722–735. Springer, Heidelberg (2007).  https://doi.org/10.1007/978-3-540-76298-0_52CrossRefGoogle Scholar
  5. 5.
    Bahdanau, D., Cho, K., Bengio, Y.: Neural machine translation by jointly learning to align and translate. In: International Conference on Learning Representations (ICLR) (2015)Google Scholar
  6. 6.
    Bizer, C., Lehmann, J., Kobilarov, G., Auer, S., Becker, C., Cyganiak, R., Hellmann, S.: Dbpedia-a crystallization point for the web of data. Web Semant. Sci. Serv. Agents World Wide Web 7(3), 154–165 (2009)CrossRefGoogle Scholar
  7. 7.
    Chen, D., Fisch, A., Weston, J., Bordes, A.: Reading Wikipedia to answer open-domain questions. In: ACL, vol. 1, pp. 1870–1879 (2017)Google Scholar
  8. 8.
    Del Corro, L., Gemulla, R.: ClausIE: clause-based open information extraction. In: Proceedings of International Conference on World Wide Web, pp. 355–366 (2013)Google Scholar
  9. 9.
    Gupta, R., Halevy, A., Wang, X., Whang, S.E., Wu, F.: Biperpedia: an ontology for search applications. Proc. VLDB Endow. 7(7), 505–516 (2014)CrossRefGoogle Scholar
  10. 10.
    He, L., Lewis, M., Zettlemoyer, L.: Question-answer driven semantic role labeling: using natural language to annotate natural language. In: EMNLP, pp. 643–653 (2015)Google Scholar
  11. 11.
    Hermann, K.M., et al.: Teaching machines to read and comprehend. In: Advances in Neural Information Processing Systems, pp. 1693–1701 (2015)Google Scholar
  12. 12.
    Hewlett, D., et al.: WikiReading: a novel large-scale language understanding task over Wikipedia. In: ACL, vol. 1, pp. 1535–1545 (2016)Google Scholar
  13. 13.
    Hill, F., Bordes, A., Chopra, S., Weston, J.: The goldilocks principle: reading children’s books with explicit memory representations. arXiv preprint arXiv:1511.02301 (2015)
  14. 14.
    Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9, 1735–1780 (1997)CrossRefGoogle Scholar
  15. 15.
    Hu, M., Peng, Y., Qiu, X.: Reinforced mnemonic reader for machine comprehension. CoRR, abs/1705.02798 (2017)Google Scholar
  16. 16.
    Ji, H., Grishman, R.: Knowledge base population: successful approaches and challenges. In: ACL, pp. 1148–1158 (2011)Google Scholar
  17. 17.
    Kim, Y., Jernite, Y., Sontag, D., Rush, A.M.: Character-aware neural language models. In: AAAI, pp. 2741–2749 (2016)Google Scholar
  18. 18.
    Lample, G., Ballesteros, M., Subramanian, S., Kawakami, K., Dyer, C.: Neural architectures for named entity recognition. In: Proceedings of NAACL-HLT, pp. 260–270 (2016)Google Scholar
  19. 19.
    Le, Q.V., Jaitly, N., Hinton, G.E.: A simple way to initialize recurrent networks of rectified linear units. arXiv preprint arXiv:1504.00941 (2015)
  20. 20.
    Lee, T., Wang, Z., Wang, H., Hwang, S.W.: Attribute extraction and scoring: a probabilistic approach. In: 29th International Conference on Data Engineering, pp. 194–205 (2013)Google Scholar
  21. 21.
    Levy, O., Seo, M., Choi, E., Zettlemoyer, L.: Zero-shot relation extraction via reading comprehension. In: CoNLL, pp. 333–342 (2017)Google Scholar
  22. 22.
    Lin, Y., Liu, Z., Sun, M., Liu, Y., Zhu, X.: Learning entity and relation embeddings for knowledge graph completion. In: AAAI, vol. 15, pp. 2181–2187 (2015)Google Scholar
  23. 23.
    Ling, X., Weld, D.S.: Fine-grained entity recognition. In: AAAI (2012)Google Scholar
  24. 24.
    Liu, X., Shen, Y., Duh, K., Gao, J.: Stochastic answer networks for machine reading comprehension. arXiv preprint arXiv:1712.03556 (2017)
  25. 25.
    Luo, G., Huang, X., Lin, C.Y., Nie, Z.: Joint entity recognition and disambiguation. In: EMNLP, pp. 879–888 (2015)Google Scholar
  26. 26.
    Ma, X., Hovy, E.: End-to-end sequence labeling via bi-directional LSTM-CNNs-CRF. In: ACL, vol. 1, pp. 1064–1074 (2016)Google Scholar
  27. 27.
    Mintz, M., Bills, S., Snow, R., Jurafsky, D.: Distant supervision for relation extraction without labeled data. In: ACL, pp. 1003–1011 (2009)Google Scholar
  28. 28.
    Moro, A., Raganato, A., Navigli, R.: Entity linking meets word sense disambiguation: a unified approach. TACL 2, 231–244 (2014)Google Scholar
  29. 29.
    Pan, B., Li, H., Zhao, Z., Cao, B., Cai, D., He, X.: MEMEN: multi-layer embedding with memory networks for machine comprehension. arXiv preprint arXiv:1707.09098 (2017)
  30. 30.
    Pennington, J., Socher, R., Manning, C.: GloVe: global vectors for word representation. In: EMNLP, pp. 1532–1543 (2014)Google Scholar
  31. 31.
    Pyysalo, S., Ginter, F., Heimonen, J., Björne, J., Boberg, J., Järvinen, J., Salakoski, T.: BioInfer: a corpus for information extraction in the biomedical domain. BMC Bioinform. 8(1), 50 (2007)CrossRefGoogle Scholar
  32. 32.
    Rajpurkar, P., Zhang, J., Lopyrev, K., Liang, P.: SQuAD: 100,000+ questions for machine comprehension of text. In: EMNLP, pp. 2383–2392 (2016)Google Scholar
  33. 33.
    Ren, X., et al.: CoType: joint extraction of typed entities and relations with knowledge bases. In: Proceedings of the 26th International Conference on World Wide Web, pp. 1015–1024 (2017)Google Scholar
  34. 34.
    Riedel, S., Yao, L., McCallum, A.: Modeling relations and their mentions without labeled text. In: Balcázar, J.L., Bonchi, F., Gionis, A., Sebag, M. (eds.) ECML PKDD 2010. LNCS (LNAI), vol. 6323, pp. 148–163. Springer, Heidelberg (2010).  https://doi.org/10.1007/978-3-642-15939-8_10CrossRefGoogle Scholar
  35. 35.
    Roth, B., Conforti, C., Poerner, N., Karn, S., Schütze, H.: Neural architectures for open-type relation argument extraction. arXiv preprint arXiv:1803.01707 (2018)
  36. 36.
    Schmitz, M., Bart, R., Soderland, S., Etzioni, O., et al.: Open language learning for information extraction. In: EMNLP, pp. 523–534 (2012)Google Scholar
  37. 37.
    Seo, M., Kembhavi, A., Farhadi, A., Hajishirzi, H.: Bidirectional attention flow for machine comprehension. arXiv preprint arXiv:1611.01603 (2016)
  38. 38.
    Shen, W., Wang, J., Han, J.: Entity linking with a knowledge base: issues, techniques, and solutions. IEEE Trans. Knowl. Data Eng. 27(2), 443–460 (2015)CrossRefGoogle Scholar
  39. 39.
    Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15(1), 1929–1958 (2014)MathSciNetzbMATHGoogle Scholar
  40. 40.
    Srivastava, R.K., Greff, K., Schmidhuber, J.: Highway networks. arXiv preprint arXiv:1505.00387 (2015)
  41. 41.
    Stanovsky, G., Dagan, I.: Creating a large benchmark for open information extraction. In: EMNLP, pp. 2300–2305 (2016)Google Scholar
  42. 42.
    Sukhbaatar, S., Weston, J., Fergus, R., et al.: End-to-end memory networks. In: Advances in Neural Information Processing Systems, pp. 2440–2448 (2015)Google Scholar
  43. 43.
    Tjong Kim Sang, E.F., De Meulder, F.: Introduction to the CoNLL-2003 shared task: language-independent named entity recognition. In: Proceedings of NAACL-HLT, pp. 142–147 (2003)Google Scholar
  44. 44.
    Vinyals, O., Fortunato, M., Jaitly, N.: Pointer networks. In: Advances in Neural Information Processing Systems, pp. 2692–2700 (2015)Google Scholar
  45. 45.
    Vrandečić, D., Krötzsch, M.: Wikidata: a free collaborative knowledgebase. Commun. ACM 57(10), 78–85 (2014)CrossRefGoogle Scholar
  46. 46.
    Wang, S., Jiang, J.: Machine comprehension using match-LSTM and answer pointer. arXiv preprint arXiv:1608.07905 (2016)
  47. 47.
    Wang, W., Yang, N., Wei, F., Chang, B., Zhou, M.: Gated self-matching networks for reading comprehension and question answering. In: ACL, vol. 1, pp. 189–198 (2017)Google Scholar
  48. 48.
    Xu, K., Feng, Y., Huang, S., Zhao, D.: Semantic relation classification via convolutional neural networks with simple negative sampling. In: EMNLP, pp. 536–540 (2015)Google Scholar
  49. 49.
    Yahya, M., Whang, S., Gupta, R., Halevy, A.: ReNoun: fact extraction for nominal attributes. In: EMNLP, pp. 325–335 (2014)Google Scholar
  50. 50.
    Zeiler, M.D.: ADADELTA: an adaptive learning rate method. arXiv preprint arXiv:1212.5701 (2012)
  51. 51.
    Zeng, D., Liu, K., Chen, Y., Zhao, J.: Distant supervision for relation extraction via piecewise convolutional neural networks. In: EMNLP, pp. 1753–1762 (2015)Google Scholar
  52. 52.
    Zheng, S., Wang, F., Bao, H., Hao, Y., Zhou, P., Xu, B.: Joint extraction of entities and relations based on a novel tagging scheme. In: ACL, vol. 1, pp. 1227–1236 (2017)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Lin Qiu
    • 1
  • Hao Zhou
    • 1
  • Yanru Qu
    • 1
  • Weinan Zhang
    • 1
  • Suoheng Li
    • 2
  • Shu Rong
    • 2
  • Dongyu Ru
    • 1
  • Lihua Qian
    • 1
  • Kewei Tu
    • 3
  • Yong Yu
    • 1
  1. 1.Shanghai Jiao Tong UniversityShanghaiChina
  2. 2.Yitu TechShanghaiChina
  3. 3.ShanghaiTech UniversityShanghaiChina

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