Skip to main content
Fachgebiete
Automobil + Motoren Bauwesen + Immobilien Business IT + Informatik Elektrotechnik + Elektronik Energie + Nachhaltigkeit Finance + Banking Management + Führung Marketing + Vertrieb Maschinenbau + Werkstoffe Versicherung + Risiko
DE
EN
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Bücher
Zeitschriften
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
MTZ-Fachkongress

Antriebe und Energiesysteme von morgen


Austausch von Automobil- und Energiewirtschaft

Am 14./15. Mai geht es in Chemnitz um die Mobilitätswende durch Elektro- und Wasserstofffahrzeuge.
Erweiterte Suche
Anmelden
nach oben
Erschienen in:
Fine-Grained Evaluation of Rule- and Embedding-Based Systems for Knowledge Graph Completion Kapitel lesen Erstes Kapitel lesen

2018 | OriginalPaper | Buchkapitel

QA4IE: A Question Answering Based Framework for Information Extraction

verfasst von : Lin Qiu, Hao Zhou, Yanru Qu, Weinan Zhang, Suoheng Li, Shu Rong, Dongyu Ru, Lihua Qian, Kewei Tu, Yong Yu

Erschienen in: The Semantic Web – ISWC 2018

Verlag: Springer International Publishing

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

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.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Jetzt informieren
Vorheriges Kapitel Enriching Knowledge Bases with Counting Quantifiers
Nächstes Kapitel Constructing a Recipe Web from Historical Newspapers
Fußnoten
1
Our source code and benchmark datasets can be found at https://​github.​com/​SJTU-lqiu/​QA4IE.
 
Literatur
1.
Abadi, M., et al.: Tensorflow: a system for large-scale machine learning. In: OSDI, vol. 16, pp. 265–283 (2016)
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)
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)
4.
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)
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)CrossRef
7.
Chen, D., Fisch, A., Weston, J., Bordes, A.: Reading Wikipedia to answer open-domain questions. In: ACL, vol. 1, pp. 1870–1879 (2017)
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)
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)CrossRef
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)
11.
Hermann, K.M., et al.: Teaching machines to read and comprehend. In: Advances in Neural Information Processing Systems, pp. 1693–1701 (2015)
12.
Hewlett, D., et al.: WikiReading: a novel large-scale language understanding task over Wikipedia. In: ACL, vol. 1, pp. 1535–1545 (2016)
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.
Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9, 1735–1780 (1997)CrossRef
15.
Hu, M., Peng, Y., Qiu, X.: Reinforced mnemonic reader for machine comprehension. CoRR, abs/1705.02798 (2017)
16.
Ji, H., Grishman, R.: Knowledge base population: successful approaches and challenges. In: ACL, pp. 1148–1158 (2011)
17.
Kim, Y., Jernite, Y., Sontag, D., Rush, A.M.: Character-aware neural language models. In: AAAI, pp. 2741–2749 (2016)
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)
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.
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)
21.
Levy, O., Seo, M., Choi, E., Zettlemoyer, L.: Zero-shot relation extraction via reading comprehension. In: CoNLL, pp. 333–342 (2017)
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)
23.
Ling, X., Weld, D.S.: Fine-grained entity recognition. In: AAAI (2012)
24.
Liu, X., Shen, Y., Duh, K., Gao, J.: Stochastic answer networks for machine reading comprehension. arXiv preprint arXiv:​1712.​03556 (2017)
25.
Luo, G., Huang, X., Lin, C.Y., Nie, Z.: Joint entity recognition and disambiguation. In: EMNLP, pp. 879–888 (2015)
26.
Ma, X., Hovy, E.: End-to-end sequence labeling via bi-directional LSTM-CNNs-CRF. In: ACL, vol. 1, pp. 1064–1074 (2016)
27.
Mintz, M., Bills, S., Snow, R., Jurafsky, D.: Distant supervision for relation extraction without labeled data. In: ACL, pp. 1003–1011 (2009)
28.
Moro, A., Raganato, A., Navigli, R.: Entity linking meets word sense disambiguation: a unified approach. TACL 2, 231–244 (2014)
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.
Pennington, J., Socher, R., Manning, C.: GloVe: global vectors for word representation. In: EMNLP, pp. 1532–1543 (2014)
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)CrossRef
32.
Rajpurkar, P., Zhang, J., Lopyrev, K., Liang, P.: SQuAD: 100,000+ questions for machine comprehension of text. In: EMNLP, pp. 2383–2392 (2016)
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)
34.
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.
Schmitz, M., Bart, R., Soderland, S., Etzioni, O., et al.: Open language learning for information extraction. In: EMNLP, pp. 523–534 (2012)
37.
Seo, M., Kembhavi, A., Farhadi, A., Hajishirzi, H.: Bidirectional attention flow for machine comprehension. arXiv preprint arXiv:​1611.​01603 (2016)
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)CrossRef
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)MathSciNetMATH
40.
41.
Stanovsky, G., Dagan, I.: Creating a large benchmark for open information extraction. In: EMNLP, pp. 2300–2305 (2016)
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)
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)
44.
Vinyals, O., Fortunato, M., Jaitly, N.: Pointer networks. In: Advances in Neural Information Processing Systems, pp. 2692–2700 (2015)
45.
Vrandečić, D., Krötzsch, M.: Wikidata: a free collaborative knowledgebase. Commun. ACM 57(10), 78–85 (2014)CrossRef
46.
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)
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)
49.
Yahya, M., Whang, S., Gupta, R., Halevy, A.: ReNoun: fact extraction for nominal attributes. In: EMNLP, pp. 325–335 (2014)
50.
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)
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)
Metadaten
Titel
QA4IE: A Question Answering Based Framework for Information Extraction
verfasst von
Lin Qiu
Hao Zhou
Yanru Qu
Weinan Zhang
Suoheng Li
Shu Rong
Dongyu Ru
Lihua Qian
Kewei Tu
Yong Yu
Copyright-Jahr
2018
Buch
The Semantic Web – ISWC 2018

Print ISBN: 978-3-030-00670-9

Electronic ISBN: 978-3-030-00671-6

Copyright-Jahr: 2018

DOI
https://doi.org/10.1007/978-3-030-00671-6_12

Premium Partner

    Bildnachweise