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:
A Fusion Model of Multi-data Sources for User Profiling in Social Media Kapitel lesen Erstes Kapitel lesen

2018 | OriginalPaper | Buchkapitel

LM Enhanced BiRNN-CRF for Joint Chinese Word Segmentation and POS Tagging

verfasst von : Jianhu Zhang, Gongshen Liu, Jie Zhou, Cheng Zhou, Huanrong Sun

Erschienen in: Natural Language Processing and Chinese Computing

Verlag: Springer International Publishing

Einloggen

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

search-config
loading …

Abstract

Word segmentation and part-of-speech tagging are two preliminary but fundamental components of Chinese natural language processing. With the upsurge of deep learning, end-to-end models are built without handcrafted features. In this work, we model Chinese word segmentation and part-of-speech tagging jointly on the basis of state-of-the-art BiRNN-CRF architecture. LSTM is adopted as the basic recurrent unit. Apart from utilizing pre-trained character embeddings and trigram features, we incorporate neural language model and conduct multi-task training. Highway layers are applied to tackle the discordance issue of the naive co-training. Experimental results on CTB5, CTB7, and PPD datasets show the effectiveness of the proposed method.

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 Recognizing Macro Chinese Discourse Structure on Label Degeneracy Combination Model
Nächstes Kapitel Chinese Grammatical Error Correction Using Statistical and Neural Models
Fußnoten
Literatur
1.
Abadi, M. et al.: Tensorflow: a system for large-scale machine learning. In: Proceedings of the 12th USENIX Conference on Operating Systems Design and Implementation, OSDI 2016, pp. 265–283. USENIX Association, Berkeley (2016)
2.
Chen, X., Qiu, X., Huang, X.: A long dependency aware deep architecture for joint Chinese word segmentation and POS tagging. CoRR abs/1611.05384 (2016)
3.
Chung, J., Gülçehre, Ç., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. CoRR abs/1412.3555 (2014)
4.
Duchi, J., Hazan, E., Singer, Y.: Adaptive subgradient methods for online learning and stochastic optimization. J. Mach. Learn. Res. 12, 2121–2159 (2011)MathSciNetMATH
5.
Durme, B.V., Rastogi, P., Poliak, A., Martin, M.P.: Efficient, compositional, order-sensitive n-gram embeddings. In: EACL (2017)
6.
Glorot, X., Bengio, Y.: Understanding the difficulty of training deep feedforward neural networks. In: Teh, Y.W., Titterington, M. (eds.) Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, Proceedings of Machine Learning Research, PMLR, Chia Laguna Resort, Sardinia, Italy, vol. 9, pp. 249–256, 13–15 May 2010
7.
8.
Jiang, W., Huang, L., Liu, Q., Lü, Y.: A cascaded linear model for joint chinese word segmentation and part-of-speech tagging. In: Proceedings of the 46th Annual Meeting of the Association for Computational Linguistics (2008)
9.
Jin, G., Chen, X.: The fourth international Chinese language processing bakeoff: Chinese word segmentation, named entity recognition and Chinese POS tagging. In: IJCNLP (2008)
10.
11.
Lafferty, J.D., McCallum, A., Pereira, F.C.N.: Conditional random fields: probabilistic models for segmenting and labeling sequence data. In: Proceedings of the Eighteenth International Conference on Machine Learning, ICML 2001, pp. 282–289. Morgan Kaufmann Publishers Inc., San Francisco (2001)
12.
Li, B., Liu, T., Zhao, Z., Wang, P., Du, X.: Neural bag-of-ngrams. In: AAAI, pp. 3067–3074 (2017)
13.
Li, Y., Li, W., Sun, F., Li, S.: Component-enhanced Chinese character embeddings. CoRR abs/1508.06669 (2015)
14.
Ling, W., Luís, T., Marujo, L., Astudillo, R.F., Amir, S., Dyer, C., Black, A.W., Trancoso, I.: Finding function in form: Compositional character models for open vocabulary word representation. CoRR abs/1508.02096 (2015)
15.
Liu, L., Shang, J., Xu, F.F., Ren, X., Gui, H., Peng, J., Han, J.: Empower sequence labeling with task-aware neural language model. CoRR abs/1709.04109 (2017)
16.
Ma, X., Hovy, E.H.: End-to-end sequence labeling via bi-directional LSTM-CNNs-CRF. CoRR abs/1603.01354 (2016)
17.
McCallum, A., Freitag, D., Pereira, F.C.N.: Maximum entropy markov models for information extraction and segmentation. In: Proceedings of the Seventeenth International Conference on Machine Learning, ICML 2000, pp. 591–598. Morgan Kaufmann Publishers Inc., San Francisco (2000)
18.
Ng, H.T., Low, J.K.: Chinese part-of-speech tagging: one-at-a-time or all-at-once? word-based or character-based? In: Lin, D., Wu, D. (eds.) Proceedings of EMNLP 2004, pp. 277–284. Association for Computational Linguistics, Barcelona, July 2004
19.
Pascanu, R., Mikolov, T., Bengio, Y.: Understanding the exploding gradient problem. CoRR abs/1211.5063 (2012)
20.
Peng, F., Feng, F., McCallum, A.: Chinese segmentation and new word detection using conditional random fields. In: Proceedings of the 20th International Conference on Computational Linguistics, COLING 2004. Association for Computational Linguistics, Stroudsburg (2004). https://​doi.​org/​10.​3115/​1220355.​1220436
21.
Pennington, J., Socher, R., Manning, C.D.: Glove: global vectors for word representation. In: Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014)
22.
Peters, M.E., Ammar, W., Bhagavatula, C., Power, R.: Semi-supervised sequence tagging with bidirectional language models. CoRR abs/1705.00108 (2017)
23.
Rei, M.: Semi-supervised multitask learning for sequence labeling. CoRR abs/1704.07156 (2017)
24.
Shao, Y., Hardmeier, C., Tiedemann, J., Nivre, J.: Character-based joint segmentation and POS tagging for Chinese using bidirectional RNN-CRF. CoRR abs/1704.01314 (2017)
25.
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, 1929–1958 (2014)MathSciNetMATH
26.
Srivastava, R.K., Greff, K., Schmidhuber, J.: Highway networks. CoRR abs/1505.00387 (2015)
27.
Sun, Y., Lin, L., Tang, D., Yang, N., Ji, Z., Wang, X.: Radical-enhanced Chinese character embedding. CoRR abs/1404.4714 (2014)
28.
Uchiumi, K., Tsukahara, H., Mochihashi, D.: Inducing word and part-of-speech with Pitman-Yor hidden semi-Markov models. In: ACL (2015)
29.
Vajjala, S., Banerjee, S.: A study of n-gram and embedding representations for native language identification. In: Proceedings of the 12th Workshop on Innovative Use of NLP for Building Educational Applications, pp. 240–248 (2017)
30.
Wang, Y., Kazama, J., Tsuruoka, Y., Chen, W., Zhang, Y., Torisawa, K.: Improving Chinese word segmentation and POS tagging with semi-supervised methods using large auto-analyzed data. In: IJCNLP (2011)
31.
Wieting, J., Bansal, M., Gimpel, K., Livescu, K.: Charagram: Embedding words and sentences via character n-grams. CoRR abs/1607.02789 (2016)
32.
Yang, Z., Salakhutdinov, R., Cohen, W.W.: Transfer learning for sequence tagging with hierarchical recurrent networks. CoRR abs/1703.06345 (2017)
33.
Zhang, Y., Clark, S.: Joint word segmentation and POS tagging using a single perceptron. In: Proceedings of ACL-08: HLT, pp. 888–896. Association for Computational Linguistics, Columbus, June 2008
34.
Zhang, Y., Clark, S.: A fast decoder for joint word segmentation and POS-tagging using a single discriminative model. In: Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing, EMNLP 2010, pp. 843–852. Association for Computational Linguistics, Stroudsburg (2010)
Metadaten
Titel
LM Enhanced BiRNN-CRF for Joint Chinese Word Segmentation and POS Tagging
verfasst von
Jianhu Zhang
Gongshen Liu
Jie Zhou
Cheng Zhou
Huanrong Sun
Copyright-Jahr
2018
Buch
Natural Language Processing and Chinese Computing

Print ISBN: 978-3-319-99500-7

Electronic ISBN: 978-3-319-99501-4

Copyright-Jahr: 2018

DOI
https://doi.org/10.1007/978-3-319-99501-4_9

Premium Partner

    Bildnachweise