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:
Building Knowledge for the Purpose of Lip Speech Identification Kapitel lesen Erstes Kapitel lesen

2017 | OriginalPaper | Buchkapitel

Active Learning-Based Approach for Named Entity Recognition on Short Text Streams

verfasst von : Cuong Van Tran, Tuong Tri Nguyen, Dinh Tuyen Hoang, Dosam Hwang, Ngoc Thanh Nguyen

Erschienen in: Multimedia and Network Information Systems

Verlag: Springer International Publishing

Einloggen

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

search-config
loading …

Abstract

The named entity recognition (NER) problem has an important role in many natural language processing (NLP) applications and is one of the fundamental tasks for building NLP systems. Supervised learning methods can achieve high performance but they require a large amount of training data that is time-consuming and expensive to obtain. Active learning (AL) is well-suited to many problems in NLP, where unlabeled data may be abundant but labeled data is limited. The AL method aims to minimize annotation costs while maximizing the desired performance from the model. This study proposes a method to classify named entities from Tweet streams on Twitter by using an AL method with different query strategies. The samples were queried for labeling by human annotators based on query by committee and diversity-based querying. The experiments evaluated the proposed method on Tweet data and achieved promising results that proved better than the baseline.

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 Unsupervised Construction of Quasi-comparable Corpora and Probing for Parallel Textual Data
Nächstes Kapitel Prediction of Topics Popularity on On-Line Social Networks
Fußnoten
Literatur
1.
Abdallah, S., Shaalan, K., Shoaib, M.: Integrating rule-based system with classification for arabic named entity recognition. In: Computational Linguistics and Intelligent Text Processing, pp. 311–322. Springer (2012)
2.
Cano Basave, A.E., Varga, A., Rowe, M., Stankovic, M., Dadzie, A.S.: Making sense of microposts (#msm2013) concept extraction challenge (2013)
3.
Chen, H.H., Ding, Y.W., Tsai, S.C.: Named entity extraction for information retrieval. Comput. Process. Orient. Lang. 12(1), 75–85 (1998)
4.
Chen, Y., Lasko, T.A., Mei, Q., Denny, J.C., Xu, H.: A study of active learning methods for named entity recognition in clinical text. J. Biomed. Inf. 58, 11–18 (2015)CrossRef
5.
Giao, B.C., Anh, D.T.: Similarity search for numerous patterns over multiple time series streams under dynamic time warping which supports data normalization. Vietnam J. Comput. Sci. pp. 1–16 (2016)
6.
Hassanzadeh, H., Keyvanpour, M.: A variance based active learning approach for named entity recognition. In: Intelligent Computing and Information Science, pp. 347–352. Springer (2011)
7.
Li, C., Weng, J., He, Q., Yao, Y., Datta, A., Sun, A., Lee, B.S.: Twiner: named entity recognition in targeted twitter stream. In: Proceedings of the 35th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 721–730. ACM (2012)
8.
Liu, X., Zhang, S., Wei, F., Zhou, M.: Recognizing named entities in tweets. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, vol. 1. pp. 359–367. Association for Computational Linguistics (2011)
9.
Meyer, C., Schramm, H.: Boosting hmm acoustic models in large vocabulary speech recognition. Speech Commun. 48(5), 532–548 (2006)CrossRef
10.
Nobata, C., Sekine, S., Isahara, H., Grishman, R.: Summarization system integrated with named entity tagging and ie pattern discovery. In: Proceedings of Third International Conference on Language Resources and Evaluation, pp. 1742–1745 (2002)
11.
Olsson, F.: A literature survey of active machine learning in the context of natural language processing (2009)
12.
Ritter, A., Clark, S., Etzioni, O., et al.: Named entity recognition in tweets: an experimental study. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing, pp. 1524–1534. Association for Computational Linguistics (2011)
13.
Settles, B.: Active learning literature survey. Univ. Wis. Madison 52(55–66), 11 (2010)
14.
Stahl, F., Schomm, F., Vossen, G., Vomfell, L.: A classification framework for data marketplaces. Vietnam J. Comput. Sci. pp. 1–7 (2016)
15.
Tran, T., Nguyen, D.T.: Algorithm of computing verbal relationships for generating vietnamese paragraph of summarization from the logical expression of discourse representation structure. Vietnam J. Comput. Sci. pp. 1–12 (2015)
16.
Tran, V.C., Hwang, D., Jung, J.J.: Semi-supervised approach based on co-occurrence coefficient for named entity recognition on twitter. In: 2015 2nd National Foundation for Science and Technology Development Conference on Information and Computer Science (NICS), pp. 141–146. IEEE (2015)
17.
Yao, L., Sun, C., Wang, X., Wang, X.: Combining self learning and active learning for chinese named entity recognition. J. Softw. 5(5), 530–537 (2010)
Metadaten
Titel
Active Learning-Based Approach for Named Entity Recognition on Short Text Streams
verfasst von
Cuong Van Tran
Tuong Tri Nguyen
Dinh Tuyen Hoang
Dosam Hwang
Ngoc Thanh Nguyen
Copyright-Jahr
2017
Buch
Multimedia and Network Information Systems

Print ISBN: 978-3-319-43981-5

Electronic ISBN: 978-3-319-43982-2

Copyright-Jahr: 2017

DOI
https://doi.org/10.1007/978-3-319-43982-2_28

Premium Partner

    Bildnachweise