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
Bücher
Zeitschriften
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
SLX-Digitalkonferenz: Zukunftswerkstatt 2024

Mehr Umsatz mit Top-Kontakten

Am 7. Mai erfahren Sie, wie Vertriebsteams Leadgenerierung, Leadnurturing und Leadstrategien effizient steuern können.
Erweiterte Suche
Anmelden
nach oben
Erschienen in:
Integrating Structural Context with Local Context for Disambiguating Word Senses Kapitel lesen Erstes Kapitel lesen

2016 | OriginalPaper | Buchkapitel

Character-Based LSTM-CRF with Radical-Level Features for Chinese Named Entity Recognition

verfasst von : Chuanhai Dong, Jiajun Zhang, Chengqing Zong, Masanori Hattori, Hui Di

Erschienen in: Natural Language Understanding and Intelligent Applications

Verlag: Springer International Publishing

Einloggen

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

search-config
loading …

Abstract

State-of-the-art systems of Chinese Named Entity Recognition (CNER) require large amounts of hand-crafted features and domain-specific knowledge to achieve high performance. In this paper, we apply a bidirectional LSTM-CRF neural network that utilizes both character-level and radical-level representations. We are the first to use character-based BLSTM-CRF neural architecture for CNER. By contrasting the results of different variants of LSTM blocks, we find the most suitable LSTM block for CNER. We are also the first to investigate Chinese radical-level representations in BLSTM-CRF architecture and get better performance without carefully designed features. We evaluate our system on the third SIGHAN Bakeoff MSRA data set for simplfied CNER task and achieve state-of-the-art performance 90.95% F1.

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 Learning Word Sense Embeddings from Word Sense Definitions
Nächstes Kapitel Improving First Order Temporal Fact Extraction with Unreliable Data
Fußnoten
Literatur
1.
Bengio, Y., Simard, P., Frasconi, P.: Learning long-term dependencies with gradient descent is difficult. IEEE Trans. Neural Netw. 5, 157–166 (1994)CrossRef
2.
Chen, A., Peng, F., Shan, R., Sun, G.: Chinese named entity recognition with conditional probabilistic models. In: Proceedings of 5th SIGHAN Workshop on Chinese Language Processing, pp. 173–176 (2006)
3.
Chieu, H.L., Ng, H.T.: Named entity recognition: a maximum entropy approach using global information. In: Proceedings of 19th International Conference on Computational Linguistics, vol. 1, pp. 1–7. Association for Computational Linguistics, Morristown (2002)
4.
Chiu, J.P.C., Nichols, E.: Named entity recognition with bidirectional LSTM-CNNs. In: Transactions of the ACL, pp. 1–9 (2015)
5.
Cho, K., van Merrienboer, B., Bahdanau, D., Bengio, Y.: On the properties of neural machine translation: encoder-decoder approaches. In: SSST-8, pp. 103–111 (2014)
6.
Collobert, R., Weston, J., Bottou, L., Karlen, M., Kavukcuoglu, K., Kuksa, P.: Natural language processing (almost) from scratch. J. Mach. Learn. Res. 12, 2493–2537 (2011)MATH
7.
Duan, H., Zheng, Y.: A study on features of the CRFs-based Chinese. Int. J. Adv. Intell. 3, 287–294 (2011)
8.
Fu, G., Luke, K.K.: Chinese named entity recognition using lexicalized HMMs. ACM SIGKDD Explor. Newsl. 7, 19–25 (2005)CrossRef
9.
Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: LSTM: a search space odyssey. p. 10 (2015). arXiv
10.
Han, A.L.-F., Wong, D.F., Chao, L.S.: Chinese named entity recognition with conditional random fields in the light of Chinese characteristics. In: Kłopotek, M.A., Koronacki, J., Marciniak, M., Mykowiecka, A., Wierzchoń, S.T. (eds.) IIS 2013. LNCS, vol. 7912, pp. 57–68. Springer, Heidelberg (2013). doi:10.​1007/​978-3-642-38634-3_​8 CrossRef
11.
12.
Hinton, G.E., Srivastava, N., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.R.: Improving neural networks by preventing co-adaptation of feature detectors. pp. 1–18 (2012). arXiv e-prints
13.
Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9, 1735–1780 (1997)CrossRef
14.
Huang, Z., Xu, W., Yu, K.: Bidirectional LSTM-CRF models for sequence tagging (2015). arXiv
15.
Jozefowicz, R., Zaremba, W., Sutskever, I.: An empirical exploration of recurrent network architectures. In: Proceedings of 32nd International Conference on Machine Learning, pp. 2342–2350 (2015)
16.
Lafferty, J., McCallum, A., Pereira, F.: Conditional random fields: probabilistic models for segmenting and labeling sequence data. In: Proceedings of 18th International Conference on Machine Learning, ICML 2001, pp. 282–289 (2001)
17.
Lample, G., Ballesteros, M., Subramanian, S., Kawakami, K., Dyer, C.: Neural architectures for named entity recognition. pp. 1–10 (2016). arXiv
18.
Levow, G.A.: The third international Chinese language processing bakeoff: word segmentation and named entity recognition. In: Computational Linguistics, pp. 108–117 (2006)
19.
Li, L., Mao, T., Huang, D., Yang, Y.: Hybrid models for Chinese named entity recognition. In: Proceedings of 5th SIGHAN Workshop on Chinese Language Processing, pp. 72–78 (2006)
20.
Li, W., Li, J., Tian, Y., Sui, Z.: Fine-grained classification of named entities by fusing multi-features. pp. 693–702 (2012)
21.
Li, Y., Li, W., Sun, F., Li, S.: Component-enhanced Chinese character embeddings. In: EMNLP, pp. 829–834 (2015)
22.
23.
Mikolov, T., Corrado, G., Chen, K., Dean, J.: Efficient estimation of word representations in vector space. In: Proceedings of International Conference on Learning Representations (ICLR 2013), pp. 1–12 (2013)
24.
Mikolov, T., Sutskever, I., Chen, K., Corrado, G., Dean, J.: Distributed representations of words and phrases and their compositionality. In: NIPS, pp. 1–9 (2013)
25.
26.
Shi, X., Zhai, J., Yang, X., Xie, Z., Liu, C.: Radical embedding: delving deeper to Chinese radicals. In: Proceedings of 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (vol. 1: Long Papers), pp. 594–598 (2015)
27.
Sun, Y., Lin, L., Yang, N., Ji, Z., Wang, X.: Radical-enhanced chinese character embedding. In: Loo, C.K., Yap, K.S., Wong, K.W., Teoh, A., Huang, K. (eds.) ICONIP 2014. LNCS, vol. 8835, pp. 279–286. Springer, Heidelberg (2014). doi:10.​1007/​978-3-319-12640-1_​34
28.
Yang, Z., Salakhutdinov, R., Cohen, W.: Multi-task cross-lingual sequence tagging from scratch (2016). arXiv preprint arXiv:​1603.​06270
29.
Zhang, S., Qin, Y., Wen, J., Wang, X.: Word segmentation and named entity recognition for SIGHAN Bakeoff3. In: Proceedings of 5th SIGHAN Workshop on Chinese Language Processing, pp. 158–161 (2006)
30.
Zhang, Y., Clark, S.: A fast decoder for joint word segmentation and POS-tagging using a single discriminative model. In: Proceedings of 2010 Conference on Empirical Methods in Natural Language Processing, pp. 843–852 (2010)
31.
Zhou, J., He, L., Dai, X., Chen, J.: Chinese named entity recognition with a multi-phase model. In: Proceedings of 5th SIGHAN Workshop on Chinese Language Processing, pp. 213–216 (2006)
32.
Zhou, J., Qu, W., Zhang, F.: Chinese named entity recognition via joint identification and categorization. Chin. J. Electron. 22, 225–230 (2013)
Metadaten
Titel
Character-Based LSTM-CRF with Radical-Level Features for Chinese Named Entity Recognition
verfasst von
Chuanhai Dong
Jiajun Zhang
Chengqing Zong
Masanori Hattori
Hui Di
Copyright-Jahr
2016
Buch
Natural Language Understanding and Intelligent Applications

Print ISBN: 978-3-319-50495-7

Electronic ISBN: 978-3-319-50496-4

Copyright-Jahr: 2016

DOI
https://doi.org/10.1007/978-3-319-50496-4_20