Part of Speech Tagging: Shallow or Deep Learning?

Main Article Content

Robert Östling

Abstract

Deep neural networks have advanced the state of the art in numerous fields, but they generally suffer from low computational efficiency and the level of improvement compared to more efficient machine learning models is not always significant. We perform a thorough PoS tagging evaluation on the Universal Dependencies treebanks, pitting a state-of-the-art neural network approach against UDPipe and our sparse structured perceptron-based tagger, efselab. In terms of computational efficiency, efselab is three orders of magnitude faster than the neural network model, while being more accurate than either of the other systems on 47 of 65 treebanks.

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Author Biography

Robert Östling, Stockholm University, Department of Linguistics

Assistant Professor, Department of Linguistics

References

Appleyard, Jeremy, Tomás Kociský, and Phil Blunsom. 2016. Optimizing performance of recurrent neural networks on GPUs. CoRR abs/1604.01946.

Bahdanau, Dzmitry, Kyunghyun Cho, and Yoshua Bengio. 2014. Neural machine translation by jointly learning to align and translate. CoRR abs/1409.0473.

Chen, Danqi and Christopher Manning. 2014. A fast and accurate dependency parser using neural networks. In Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 740–750. Doha, Qatar: Association for Computational Linguistics. https://doi.org/10.3115/v1/D14-1082

Collins, Michael. 2002. Discriminative training methods for hidden markov models: Theory and experiments with perceptron algorithms. In Proceedings of the ACL-02 Conference on Empirical Methods in Natural Language Processing - Volume 10, EMNLP ’02, pages 1–8. Stroudsburg, PA, USA: Association for Computational Linguistics. https://doi.org/10.3115/1118693.1118694

Daumé, Hal, III and Daniel Marcu. 2005. Learning as search optimization: Approximate large margin methods for structured prediction. In Proceedings of the 22Nd International Conference on Machine Learning, ICML ’05, pages 169–176. New York, NY, USA: ACM. ISBN 1-59593-180-5. https://doi.org/10.1145/1102351.1102373

Ganchev, Kuzman and Mark Dredze. 2008. Small statistical models by random feature mixing. In Proceedings of the ACL-2008 Workshop on Mobile Language Processing. Association for Computational Linguistics.

Goodfellow, Ian J., Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron C. Courville, and Yoshua Bengio. 2014. Generative adversarial networks. CoRR abs/1406.2661.

He, Kaiming, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. 2016. Identity mappings in deep residual networks. CoRR abs/1603.05027.

Hochreiter, Sepp and Jürgen Schmidhuber. 1997. Long short-term memory. Neural Computation 9(8):1735–1780. https://doi.org/10.1162/neco.1997.9.8.1735

Lample, Guillaume and Devendra Singh Chaplot. 2017. Playing FPS games with deep reinforcement learning. In Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence, February 4-9, 2017, San Francisco, California, USA., pages 2140–2146.

Neubig, Graham, Chris Dyer, Yoav Goldberg, Austin Matthews, Waleed Ammar, Antonios Anastasopoulos, Miguel Ballesteros, David Chiang, Daniel Clothiaux, Trevor Cohn, Kevin Duh, Manaal Faruqui, Cynthia Gan, Dan Garrette, Yangfeng Ji, Lingpeng Kong, Adhiguna Kuncoro, Gaurav Kumar, Chaitanya Malaviya, Paul Michel, Yusuke Oda, Matthew Richardson, Naomi Saphra, Swabha Swayamdipta, and Pengcheng Yin. 2017. Dynet: The dynamic neural network toolkit. CoRR abs/1701.03980.

Nivre, Joakim et al. 2017. Universal dependencies 2.0 – CoNLL 2017 shared task development and test data. LINDAT/CLARIN digital library at the Institute of Formal and Applied Linguistics, Charles University.

Nivre, Joakim, Johan Hall, Jens Nilsson, Atanas Chanev, Gülsen Eryigit, Sandra Kübler, Svetoslav Marinov, and Erwin Marsi. 2007. MaltParser: A language-independent system for data-driven dependency parsing. Natural Language Engineering 13:95–135.

Plank, Barbara, Anders Søgaard, and Yoav Goldberg. 2016. Multilingual part-of-speech tagging with bidirectional long short-term memory models and auxiliary loss. In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 412–418. Berlin, Germany: Association for Computational Linguistics. https://doi.org/10.18653/v1/P16-2067

Ratnaparkhi, Adwait. 1996. A maximum entropy model for part-of-speech tagging. In Conference on Empirical Methods in Natural Language Processing, EMNLP 1996, pages 133–142. Philadelphia, PA, USA.

Rosenblatt, Frank. 1957. The perceptron: a perceiving and recognizing automaton. Tech. rep., Cornell Aeronautical Laboratory, inc.

Shen, Libin, Giorgio Satta, and Aravind Joshi. 2007. Guided learning for bidirectional sequence classification. In Proceedings of the 45th Annual Meeting of the Association of Computational Linguistics, pages 760–767. Prague, Czech Republic: Association for Computational Linguistics.

Silver, David, Aja Huang, Chris J. Maddison, Arthur Guez, Laurent Sifre, George van den Driessche, Julian Schrittwieser, Ioannis Antonoglou, Veda Panneershelvam, Marc Lanctot, Sander Dieleman, Dominik Grewe, John Nham, Nal Kalchbrenner, Ilya Sutskever, Timothy Lillicrap, Madeleine Leach, Koray Kavukcuoglu, Thore Graepel, and Demis Hassabis. 2016. Mastering the game of Go with deep neural networks and tree search. Nature 529(7587):484–489. https://doi.org/10.1038/nature16961

Straka, Milan, Jan Hajic, and Straková Jana. 2016. UDPipe: trainable pipeline for processing CoNLL-U files performing tokenization, morphological analysis, pos tagging and parsing. In Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC’16). Paris, France: European Language Resources Association (ELRA).

Straková, Jana, Milan Straka, and Jan Hajic. 2014. Open-Source Tools for Morphology, Lemmatization, POS Tagging and Named Entity Recognition. In Proceedings of 52nd Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pages 13–18. Baltimore, Maryland: Association for Computational Linguistics. https://doi.org/10.3115/v1/P14-5003

Viterbi, Andrew. 1967. Error bounds for convolutional codes and an asymptotically optimum decoding algorithm. IEEE Transactions on Information Theory 13(2):260–269. https://doi.org/10.1109/TIT.1967.1054010

Wu, Yonghui, Mike Schuster, Zhifeng Chen, Quoc V. Le, Mohammad Norouzi, Wolfgang Macherey, Maxim Krikun, Yuan Cao, Qin Gao, Klaus Macherey, Jeff Klingner, Apurva Shah, Melvin Johnson, Xiaobing Liu, Lukasz Kaiser, Stephan Gouws, Yoshikiyo Kato, Cliff Young, Jason Smith, Jason Riesa, Alex Rudnick, Oriol Vinyals, Greg Corrado, Macduff Hughes, and Jeffrey Dean. 2016. Google’s neural machine translation system: Bridging the gap between human and machine translation. CoRR abs/1609.08144.