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:
The Use of Reference Graphs in the Entity Resolution of Criminal Networks Kapitel lesen Erstes Kapitel lesen

2016 | OriginalPaper | Buchkapitel

Multilevel Syntactic Parsing Based on Recursive Restricted Boltzmann Machines and Learning to Rank

verfasst von : Jungang Xu, Hong Chen, Shilong Zhou, Ben He

Erschienen in: Intelligence and Security Informatics

Verlag: Springer International Publishing

Einloggen

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

search-config
loading …

Abstract

Syntactic parsing is one of the central tasks in Natural Language Processing. In this paper, a multilevel syntactic parsing algorithm is proposed, which is a three-level model with innovative combinations of existing mature tools and algorithms. First, coarse-grained syntax trees are generated with general algorithms, such as Cocke-Younger-Kasami (CYK) algorithm based on Probabilistic Context Free Grammar (PCFG). Second, Recursive Restricted Boltzmann Machines (RRBM) are constructed, which aim at extracting feature vector through training syntax trees with deep learning methods. At last, Learning to Rank (LTR) model is trained to get the most satisfactory syntax tree and furthermore turn the parsing problem into a typical retrieval problem. Experiment results show that our method has achieved the state-of-the-art performance on syntactic parsing task.

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 A Profile-Based Authorship Attribution Approach to Forensic Identification in Chinese Online Messages
Nächstes Kapitel Stratified Over-Sampling Bagging Method for Random Forests on Imbalanced Data
Literatur
1.
Mnih, A., Hinton, G.: Three new graphical models for statistical language modelling. In: Proceedings of the 24th International Conference on Machine Learning, pp. 641–648. ACM (2007)
2.
Hinton, G.E., Salakhutdinov, R.R.: Reducing the dimensionality of data with neural networks. Science 313(5786), 504–507 (2006)MathSciNetCrossRefMATH
3.
Hinton, G.E., Osindero, S., The, Y.W.: A fast learning algorithm for deep belief nets. Neural Comput. 18(7), 1527–1554 (2006)MathSciNetCrossRefMATH
4.
Salakhutdinov, R., Hinton, G.E.: Deep boltzmann machines. In: International Conference on Artificial Intelligence and Statistics, pp. 448–455 (2009)
5.
Krizhevsky, A., Hinton, G.E.: Learning multiple layers of features from tiny images. Computer Science Department, University of Toronto, Technical report 1 (4), 7 (2009)
6.
Krizhevsky, A., Hinton, G.E.: Using very deep autoencoders for content-based image retrieval. In: ESANN. Citeseer (2011)
7.
Mohamed, A., Dahl, G.E., Hinton, G.: Acoustic modeling using deep belief net-works. IEEE Trans. Audio Speech Lang. Process. 20(1), 14–22 (2012)CrossRef
8.
Hinton, G.E., Deng, L., Yu, D., Dahl, G.E., Mohamed, A., Jaitly, N., Senior, A., Vanhoucke, V., Nguyen, P., Sainath, T.N., et al.: Deep neural networks for acoustic modeling in speech recognition: the shared views of four research groups. Sig. Process. Mag. IEEE 29(6), 82–97 (2012)CrossRef
9.
Salakhutdinov, R., Hinton, G.E.: Semantic hashing. Int. J. Approximate Reasoning 50(7), 969–978 (2009)CrossRef
10.
Bengio, Y., Ducharme, R., Vincent, P., et al.: A neural probabilistic language model. J. Mach. Learn. Res. 2003(3), 1137–1155 (2003)MATH
11.
Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representa-tions of words and phrases and their compositionality. In: Advances in Neural Information Processing Systems, pp. 3111–3119 (2013)
12.
Hinton, G.E.: Learning distributed representations of concepts. In: Proceedings of the Eighth Annual Conference of the Cognitive Science Society, pp. 1–12 (1986)
13.
Collobert, R., Weston, J.: A unified architecture for natural language processing: Deep neural networks with multitask learning. In: Proceedings of the 25th International Conference on Machine Learning, pp. 160–167. ACM (2008)
14.
Huang, E.H., Socher, R., Manning, C.D., et al.: Improving word representations via global con-text and multiple word prototypes. In: Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Long Papers, vol. 1, pp. 873–882. Association for Computational Linguistics (2012)
15.
Socher, R., Bauer, J., Manning, C.D., Ng, A.Y.: Parsing with compositional vector grammars. In: Proceedings of the ACL Conference (2013)
16.
Salton, G., Wong, A., Yang, C.S.: A vector space model for automatic indexing. Commun. ACM 18(11), 613–620 (1975)CrossRefMATH
17.
Manning, C.D., Raghavan, P., Schütze, H.: Introduction to Information Retrieval. Cambridge University Press, Cambridge (2008)CrossRefMATH
18.
Zhai, C.X.: Statistical language models for information retrieval. Synth. Lect. Hum. Lang. Technol. 1(1), 1–141 (2008)CrossRef
19.
Xia, F., Liu, T.Y., Wang, J., et al.: Listwise approach to learning to rank: theory and algorithm. In: Proceedings of the 25th International Conference on Machine Learning, pp. 1192–1199. ACM (2008)
20.
Gildea, D., Palmer, M.: The necessity of parsing for predicate argument recognition. In: Proceedings of the 40th Annual Meeting on Association for Computational Linguistics, ACL 2002, pp. 239–246. Association for Computational Linguistics, Stroudsburg (2002)
21.
Klein, D., Manning C.D.: Accurate unlexicalized parsing. In: Proceedings of the 41st Meeting of the Association for Computational Linguistics, pp. 423–430 (2003)
22.
Petrov, S., Barrett, L., Thibaux, R., Klein, D.: Learning accurate, compact, and interpretable tree annotation. In: Proceedings of the 21st International Conference on Computational Linguistics and the 44th Annual Meeting of the Association for Computational Linguistics, pp. 433–440. Association for Computational Linguistics (2006)
23.
Charniak, E.: A maximum-entropy-inspired parser. In: Proceedings of the 1st North American chapter of the Association for Computational Linguistics Conference, pp. 132–139. Association for Computational Linguistics (2000)
24.
25.
26.
Younger, D.H.: Recognition and parsing of context-free languages in time n 3. Inform. Control 10(2), 189 (1967)CrossRefMATH
27.
Duchi, J., Hazan, E., Singer, Y.: Adaptive subgradient methods for online learning and stochastic optimization. J. Mach. Learn. Res. 12, 2121–2159 (2011)MathSciNetMATH
28.
Abney, S., Flickenger, S., Gdaniec, C., et al.: Procedure for quantitatively comparing the syntac-tic coverage of English grammars. In: Proceedings of the Workshop on Speech and Natural Language, pp. 306–311. Association for Computational Linguistics (1991)
29.
Freund, Y., Iyer, R., Schapire, R.E., et al.: An efficient boosting algorithm for combining preferences. J. Mach. Learn. Res. 2003(4), 933–969 (2003)MathSciNetMATH
30.
Burges, C.J.C.: From ranknet to lambdarank to lambdamart: an overview. Learning 2010(11), 23–581 (2010)
31.
Xu, J., Li, H.: AdaRank: a boosting algorithm for information retrieval. In: Proceedings of the 30rd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 391–398. ACM (2007)
Metadaten
Titel
Multilevel Syntactic Parsing Based on Recursive Restricted Boltzmann Machines and Learning to Rank
verfasst von
Jungang Xu
Hong Chen
Shilong Zhou
Ben He
Copyright-Jahr
2016
Buch
Intelligence and Security Informatics

Print ISBN: 978-3-319-31862-2

Electronic ISBN: 978-3-319-31863-9

Copyright-Jahr: 2016

DOI
https://doi.org/10.1007/978-3-319-31863-9_4