Skip to main content

2020 | OriginalPaper | Buchkapitel

Learning from Partially Labeled Sequences for Behavioral Signal Annotation

verfasst von : Anna Aniszewska-Stępień, Romain Hérault, Guillaume Hacques, Ludovic Seifert, Gilles Gasso

Erschienen in: Machine Learning and Data Mining for Sports Analytics

Verlag: Springer International Publishing

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

search-config
loading …

Abstract

Herewith, we present a learning procedure that allows to deal with a partially labeled sequence dataset, i.e. when each sequence in the train dataset may contain labeled as well as unlabeled chunks. In our application case, this occurs when motor activity has been manually annotated (due to the recognition based on the video recording) and independently registered by the measuring system of high precision (touch sensors): human annotation misses some events that have been captured by the sensors. In the general setting, we aim at predicting the labels for a new fully unlabeled movement sequence, while the training has been performed on the partially labeled dataset. For this purpose we propose to use classical sequence model (hidden Markov model) that is furnished with a constrained Viterbi algorithm, which gives us a quick access to the hard approximation of the correct labeling sequences. We demonstrate, that this simple modification that constrained Viterbi provide, allows the HMM model to be trained on sparse data, and overall results in surprisingly high log-likelihood and accuracy level in annotating the partially labeled behavioral sequences in climbing. The same time we show the way to access correct labeling of the unannotated signal that can be helpful in various sport science studies for movement pattern sequential prediction.

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!

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!

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!

Literatur
1.
Zurück zum Zitat Antoniuk, V., Franc, V., Hlavac, V.: Consistency of structured output learning with missing labels. In: Holmes, G., Liu, T.Y. (eds.) JMLR: Workshop and Conference Proceedings, pp. 81–95. ACML 2015 (2015) Antoniuk, V., Franc, V., Hlavac, V.: Consistency of structured output learning with missing labels. In: Holmes, G., Liu, T.Y. (eds.) JMLR: Workshop and Conference Proceedings, pp. 81–95. ACML 2015 (2015)
2.
Zurück zum Zitat Blum, A., Mitchell, T.: Combining labeled andunlabeled data with co-training. In: Proceedings of the Workshop on Computational Learning Theory. pp. 92–100 (1998) Blum, A., Mitchell, T.: Combining labeled andunlabeled data with co-training. In: Proceedings of the Workshop on Computational Learning Theory. pp. 92–100 (1998)
3.
Zurück zum Zitat Boulanger, J., Seifert, L., Hérault, R., Coeurjolly, J.F.: Automatic sensor-based detection and classification of climbing activities. IEE Sensors J. 16(3), 742–749 (2016)CrossRef Boulanger, J., Seifert, L., Hérault, R., Coeurjolly, J.F.: Automatic sensor-based detection and classification of climbing activities. IEE Sensors J. 16(3), 742–749 (2016)CrossRef
4.
Zurück zum Zitat Cao, L., Chen, C.W.: A novel product coding and recurrent alternate decoding scheme for image transmission over noisy channels. IEEE Trans. Commun. 51, 1426–1431 (2003)CrossRef Cao, L., Chen, C.W.: A novel product coding and recurrent alternate decoding scheme for image transmission over noisy channels. IEEE Trans. Commun. 51, 1426–1431 (2003)CrossRef
5.
Zurück zum Zitat Chapelle, O., Scholkopf, B., Zien, A.: Semi-supervised Learning. MIT Press, Cambridge (2006)CrossRef Chapelle, O., Scholkopf, B., Zien, A.: Semi-supervised Learning. MIT Press, Cambridge (2006)CrossRef
6.
Zurück zum Zitat Clark, K., Luong, M.T., Manning, C., Le, Q.: Semi-supervised sequence modeling with cross-view training. In: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing. pp. 1914–1925. Association for Computational Linguistics, Brussels, Belgium (2018). https://www.aclweb.org/anthology/D18-1217 Clark, K., Luong, M.T., Manning, C., Le, Q.: Semi-supervised sequence modeling with cross-view training. In: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing. pp. 1914–1925. Association for Computational Linguistics, Brussels, Belgium (2018). https://​www.​aclweb.​org/​anthology/​D18-1217
7.
Zurück zum Zitat Dai, A., Le, Q.: Semi-supervised sequence learning. In: Proceedings of Neural Information Processing Systems Conference. pp. 1–10 (2015) Dai, A., Le, Q.: Semi-supervised sequence learning. In: Proceedings of Neural Information Processing Systems Conference. pp. 1–10 (2015)
8.
Zurück zum Zitat Dietterich, T.: Machine learning for sequential data: a review. In: Proceedings of the Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition. vol. 1, pp. 1–15 (2002) Dietterich, T.: Machine learning for sequential data: a review. In: Proceedings of the Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition. vol. 1, pp. 1–15 (2002)
9.
Zurück zum Zitat Fernandes, E., Brefeld, U.: Learning from Partially Annotated Sequences. Springer-Verlag, Berlin (2011)CrossRef Fernandes, E., Brefeld, U.: Learning from Partially Annotated Sequences. Springer-Verlag, Berlin (2011)CrossRef
10.
Zurück zum Zitat Fernandes, E., Brefeld, U., Blanco, R., Atserias, J.: Using Wikipedia for cross-language named entity recognition. In: Atzmueller, M., Chin, A., Janssen, F., Schweizer, I., Trattner, C. (eds.) Big Data Analytics in the Social and Ubiquitous Context, pp. 1–25. Springer International Publishing, Cham (2016) Fernandes, E., Brefeld, U., Blanco, R., Atserias, J.: Using Wikipedia for cross-language named entity recognition. In: Atzmueller, M., Chin, A., Janssen, F., Schweizer, I., Trattner, C. (eds.) Big Data Analytics in the Social and Ubiquitous Context, pp. 1–25. Springer International Publishing, Cham (2016)
11.
Zurück zum Zitat Hacques, G., Komar, J., Bourbousson, J., Seifert, L.: Climbers’ learning dynamics: an exploratory study. In: 4th International Rock Climbing Congress (IRCRA). Chamonix, France, 12–15th July (2018) Hacques, G., Komar, J., Bourbousson, J., Seifert, L.: Climbers’ learning dynamics: an exploratory study. In: 4th International Rock Climbing Congress (IRCRA). Chamonix, France, 12–15th July (2018)
12.
Zurück zum Zitat Huang, X., Dong, L., Boschee, E., Peng, N.: Learning a unified named entity tagger from multiple partially annotated corpora for efficient adaptation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning. pp. 515–527 (2019) Huang, X., Dong, L., Boschee, E., Peng, N.: Learning a unified named entity tagger from multiple partially annotated corpora for efficient adaptation. In: Proceedings of the 23rd Conference on Computational Natural Language Learning. pp. 515–527 (2019)
13.
Zurück zum Zitat Li, J., Liu, C., Liu, B.: Large scale sequential learning from partially labeled data. In: 2013 IEEE Seventh International Conference on Semantic Computing. pp. 176–183 (2013) Li, J., Liu, C., Liu, B.: Large scale sequential learning from partially labeled data. In: 2013 IEEE Seventh International Conference on Semantic Computing. pp. 176–183 (2013)
14.
Zurück zum Zitat Mikolov, T., Sutskever, I., Chen, K., Corrado, G., Dean, J.: Distributed representations of words and phrases and their compositionality. In: NIPS (2013) Mikolov, T., Sutskever, I., Chen, K., Corrado, G., Dean, J.: Distributed representations of words and phrases and their compositionality. In: NIPS (2013)
17.
Zurück zum Zitat Rabiner, R.: A tutorial on hidden Markov models and selected applications on speech recognition. Proc. IEEE 77(2), 257–286 (1989)CrossRef Rabiner, R.: A tutorial on hidden Markov models and selected applications on speech recognition. Proc. IEEE 77(2), 257–286 (1989)CrossRef
18.
Zurück zum Zitat Scheffer, T., Decomain, C., Wrobel, S.: Active hidden Markov models for information extraction. In: Proceedings of the 4th International Conference on Advances in Intelligent Data Analysis. pp. 309–318 (2001) Scheffer, T., Decomain, C., Wrobel, S.: Active hidden Markov models for information extraction. In: Proceedings of the 4th International Conference on Advances in Intelligent Data Analysis. pp. 309–318 (2001)
19.
Zurück zum Zitat Veeramachaneni, W., Liao, V.: A simple semi-supervised algorithm for named entity recognition. In: Workshop on Semi-supervised Learning for Natural Language Processing pp. 58–65 (2009) Veeramachaneni, W., Liao, V.: A simple semi-supervised algorithm for named entity recognition. In: Workshop on Semi-supervised Learning for Natural Language Processing pp. 58–65 (2009)
21.
Zurück zum Zitat Zhi, S., Liu, L., Zhang, Y., Wang, S., Li, Q., Zhang, C., Han, J.: Partially-typed NER datasets integration: Connecting practice to theory. (in press) pp. 1–13 (2020) Zhi, S., Liu, L., Zhang, Y., Wang, S., Li, Q., Zhang, C., Han, J.: Partially-typed NER datasets integration: Connecting practice to theory. (in press) pp. 1–13 (2020)
Metadaten
Titel
Learning from Partially Labeled Sequences for Behavioral Signal Annotation
verfasst von
Anna Aniszewska-Stępień
Romain Hérault
Guillaume Hacques
Ludovic Seifert
Gilles Gasso
Copyright-Jahr
2020
DOI
https://doi.org/10.1007/978-3-030-64912-8_11

Premium Partner