Abstract
Long short term memory (LSTM) networks have been gaining popularity in modeling sequential data such as phoneme recognition, speech translation, language modeling, speech synthesis, chatbot-like dialog systems, and others. This paper investigates the attention-based encoder-decoder LSTM networks in TIFINAGH part-of-speech (POS) tagging when it is compared to Conditional Random Fields (CRF) and Decision Tree. The attractiveness of LSTM networks is its strength in modeling long-distance dependencies. The experiment results show that Long short-term memory (LSTM) networks perform better than CRF and Decision Tree that have a near performance.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Tan, T., Ranaivo-Malançon, B., Besacier, L., Yeong, Y., Gan, K.H., Tang, E.K.: Evaluating LSTM networks, HMM and WFST in Malay part-of-speech tagging (2017). https://www.semanticscholar.org//paper/Evaluating-LSTM-Networks%2C-HMM-and-WFST-in-Malay-Tan-Ranaivo-Malan%C3%A7on/5085ceeaa3c22b3a72b7482785251c13cd6855dc. Accessed 29 Oct 2020
Zen, H., Sak, H.: Unidirectional long short-term memory recurrent neural network with recurrent output layer for low-latency speech synthesis. In: 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4470–4474 (2015). https://doi.org/10.1109/ICASSP.2015.7178816
Berard, A., Pietquin, O., Servan, C., Besacier, L.: Listen and translate: a proof of concept for end-to-end speech-to-text translation. arXiv:161201744 [cs], 6 December 2016. https://arxiv.org/abs/1612.01744. Accessed 17 Jan 2021
Samir, A., Lahbib, Z., Mohamed, O.: Amazigh PoS tagging using machine learning techniques. In: Ben Ahmed, M., Boudhir, A.A. (eds.) SCAMS 2017. LNNS, vol. 37, pp. 551–562. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-74500-8_51
Amri, S., Zenkouar, L.: Amazigh POS tagging using TreeTagger: a language independent model. In: Ezziyyani, M. (ed.) AI2SD 2018. AISC, vol. 915, pp. 622–632. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-11928-7_56
Nejme, F., Boulaknadel, S., Aboutajdine, D.: Finite state morphology for Amazigh language. In: Gelbukh, A. (ed.) CICLing 2013. LNCS, vol. 7816, pp. 189–200. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-37247-6_16
Outahajala, M., Rosso, P., Zenkouar, L.: Building an annotated corpus for Amazigh. In: Proceedings of 4th International Conference on Amazigh and ICT (2011)
Outahajala, M., Benajiba, Y., Rosso, P., Zenkouar, L.: POS tagging in Amazighe using support vector machines and conditional random fields. In: Muñoz, R., Montoyo, A., Métais, E. (eds.) NLDB 2011. LNCS, vol. 6716, pp. 238–241. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-22327-3_28
Zheng, S., Jayasumana, S., Romera-Paredes, B., et al.: Conditional random fields as recurrent neural networks, pp. 1529–1537 (2015). https://www.cv-foundation.org/openaccess/content_iccv_2015/html/Zheng_Conditional_Random_Fields_ICCV_2015_paper.html. Accessed 7 Apr 2021
Claveau, V., Ncibi, A.: Découverte de connaissances dans les séquences par CRF non-supervisés. In: 20ème Conférence Sur Le Traitement Automatique Des Langues Naturelles, TALN, vol. 1 (2013). https://hal.archives-ouvertes.fr/hal-00912314
Silfverberg, M., Ruokolainen, T., Lindén, K., Kurimo, M.: Part-of-speech tagging using conditional random fields: exploiting sub-label dependencies for improved accuracy. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 259–264. Association for Computational Linguistics (2014). https://doi.org/10.3115/v1/P14-2043
Prokhorov, S., Safronov, V.: AI for AI: what NLP techniques help researchers find the right articles on NLP. In: 2019 International Conference on Artificial Intelligence: Applications and Innovations (IC-AIAI), pp. 76–765. IEEE (2019). https://doi.org/10.1109/IC-AIAI48757.2019.00023
Samir, A., Lahbib, Z.: Training and evaluation of TreeTagger on Amazigh corpus. IJIE 6(2/3/4), 230 (2019). https://doi.org/10.1504/IJIE.2019.101130
Research Institute for Artificial Intelligence, Romanian Academy, Boros, T., Dumitrescu, S.D., Pipa, S.: Fast and accurate decision trees for natural language processing tasks. In: Recent Advances in Natural Language Processing Meet Deep Learning, RANLP 2017, pp. 103–110. Incoma Ltd. Shoumen, Bulgaria (2017). https://doi.org/10.26615/978-954-452-049-6_016
Perez-Ortiz, J.A., Forcada, M.L.: Part-of-speech tagging with recurrent neural networks. In: Proceedings of the International Joint Conference on Neural Networks, IJCNN 2001 (Cat. No. 01CH37222), vol. 3, pp. 1588–1592. IEEE (2001). https://doi.org/10.1109/IJCNN.2001.938396
Plank, B., Søgaard, A., Goldberg, Y.: Multilingual part-of-speech tagging with bidirectional long short-term memory models and auxiliary loss. arXiv:160405529 [cs], 21 July 2016. https://arxiv.org/abs/1604.05529. Accessed 29 Oct 2020
Das, D., Kolya, A., Ekbal, A., Bandyopadhyay, S.: Temporal analysis of sentiment events – a visual realization and tracking. In: Gelbukh, A.F. (ed.) CICLing 2011. LNCS, vol. 6608, pp. 417–428. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-19400-9_33
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Maarouf, O., El Ayachi, R. (2021). Part-of-Speech Tagging Using Long Short Term Memory (LSTM): Amazigh Text Written in Tifinaghe Characters. In: Fakir, M., Baslam, M., El Ayachi, R. (eds) Business Intelligence. CBI 2021. Lecture Notes in Business Information Processing, vol 416. Springer, Cham. https://doi.org/10.1007/978-3-030-76508-8_1
Download citation
DOI: https://doi.org/10.1007/978-3-030-76508-8_1
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-76507-1
Online ISBN: 978-3-030-76508-8
eBook Packages: Computer ScienceComputer Science (R0)