Abstract
Named Entity Recognition (NER) is an important subtask of information extraction that seeks to locate and recognise named entities. Despite recent achievements, we still face limitations with correctly detecting and classifying entities, prominently in short and noisy text, such as Twitter. An important negative aspect in most of NER approaches is the high dependency on hand-crafted features and domain-specific knowledge, necessary to achieve state-of-the-art results. Thus, devising models to deal with such linguistically complex contexts is still challenging. In this paper, we propose a novel multi-level architecture that does not rely on any specific linguistic resource or encoded rule. Unlike traditional approaches, we use features extracted from images and text to classify named entities. Experimental tests against state-of-the-art NER for Twitter on the Ritter dataset present competitive results (0.59 F-measure), indicating that this approach may lead towards better NER models.
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
Notes
- 1.
State-of-the-art POS tagging systems still do not have exceptional performance in short texts.
- 2.
We set N = 10 in our experiments and used Microsoft Bing as the search engine.
- 3.
scikit-learn: svm.NuSVC(nu = 0.5, kernel = ‘rbf’, gamma = 0.1, probability = True).
- 4.
bigram, in our experiments.
- 5.
pos = +1, neg = \(-1\).
- 6.
pos = +1, neg = 0.
- 7.
scikit-learn: criterion=‘entropy’, splitter=‘best’.
- 8.
References
Al-Rfou, R., Kulkarni, V., Perozzi, B., Skiena, S.: Polyglot-NER: massive multilingual named entity recognition. In: Proceedings of the 2015 SIAM International Conference on Data Mining, Vancouver, British Columbia, Canada. SIAM (2015)
Basave, A.E.C., Varga, A., Rowe, M., Stankovic, M., Dadzie, A.-S.: Making sense of microposts (#msm2013) concept extraction challenge. In: Cano, A.E., Rowe, M., Stankovic, M., Dadzie, A.-S. (eds.) CEUR Workshop Proceedings, #MSM, vol. 1019, pp. 1–15. CEUR-WS.org (2013)
Bontcheva, K., Derczynski, L., Funk, A., Greenwood, M.A., Maynard, D., Aswani, N.: Twitie: an open-source information extraction pipeline for microblog text. In: RANLP, pp. 83–90 (2013)
Chiu, J.P., Nichols, E.: Named entity recognition with bidirectional LSTM-CNNs. arXiv preprint arXiv:1511.08308 (2015)
Derczynski, L., Maynard, D., Rizzo, G., van Erp, M., Gorrell, G., Troncy, R., Petrak, J., Bontcheva, K.: Analysis of named entity recognition and linking for tweets. Inf. Process. Manage. 51(2), 32–49 (2015)
Etter, D., Ferraro, F., Cotterell, R., Buzek, O., Van Durme, B. Nerit: named entity recognition for informal text. The Johns Hopkins University, The Human Language Technology Center of Excellence, HLTCOE, 810 Wyman Park Drive, Baltimore, Maryland 21211, Technical report (2013)
Fei-Fei, L., Fergus, R., Perona, P.: Learning generative visual models from few training examples: an incremental Bayesian approach tested on 101 object categories. Comput. Vis. Image Underst. 106(1), 59–70 (2007)
Fei-Fei, L., Perona, P.: A Bayesian hierarchical model for learning natural scene categories. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, vol. 2, pp. 524–531. IEEE (2005)
Fletcher, T.: Support vector machines explained (2009). http://sutikno.blog.undip.ac.id/files/2011/11/SVM-Explained.pdf. Accessed 6 June 2013
Gattani, A., Lamba, D.S., Garera, N., Tiwari, M., Chai, X., Das, S., Subramaniam, S., Rajaraman, A., Harinarayan, V., Doan, A.: Entity extraction, linking, classification, and tagging for social media: a wikipedia-based approach. Proc. VLDB Endow. 6(11), 1126–1137 (2013)
Lample, G., Ballesteros, M., Subramanian, S., Kawakami, K., Dyer, C.: Neural architectures for named entity recognition. arXiv preprint arXiv:1603.01360 (2016)
Liu, X., Zhou, M., Wei, F., Fu, Z., Zhou, X.: Joint inference of named entity recognition and normalization for tweets. In: Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Long Papers, vol. 1, pp. 526–535. Association for Computational Linguistics (2012)
Lowe, D.G.: Object recognition from local scale-invariant features. In: The Proceedings of the Seventh IEEE International Conference on Computer Vision 1999, vol. 2, pp. 1150–1157 (1999)
MacQueen, J., et al.: Some methods for classification and analysis of multivariate observations. In: Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, Oakland, CA, USA, vol. 1, pp. 281–297 (1967)
Nadeau, D., Sekine, S.: A survey of named entity recognition and classification. Lingvisticae Investigationes 30(1), 3–26 (2007)
Tursun, O., Sinan, K.: A challenging big dataset for benchmarking trademark retrieval. In: IAPR Conference on Machine Vision and Applications (2015)
Philbin, J., Chum, O., Isard, M., Sivic, J., Zisserman, A.: Object retrieval with large vocabularies and fast spatial matching. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–8. IEEE (2007)
Ratinov, L., Roth, D.: Design challenges and misconceptions in named entity recognition. In: Proceedings of the Thirteenth Conference on Computational Natural Language Learning, pp. 147–155. Association for Computational Linguistics (2009)
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)
Roberts, A., Gaizauskas, R.J., Hepple, M., Guo, Y.: Combining terminology resources and statistical methods for entity recognition: an evaluation. In: LREC (2008)
Sivic, J., Zisserman, A.: Video Google: a text retrieval approach to object matching in videos. In: Proceedings Ninth IEEE International Conference on Computer Vision 2003, pp. 1470–1477. IEEE (2003)
Van Erp, M., Rizzo, G., Troncy, R.: Learning with the web: spotting named entities on the intersection of NERD and machine learning. In: #MSM, pp. 27–30 (2013)
Acknowledgments
This research was supported in part by an EU H2020 grant provided for the HOBBIT project (GA no. 688227) and CAPES Foundation (BEX 10179135).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG
About this paper
Cite this paper
Esteves, D., Peres, R., Lehmann, J., Napolitano, G. (2018). Named Entity Recognition in Twitter Using Images and Text. In: Garrigós, I., Wimmer, M. (eds) Current Trends in Web Engineering. ICWE 2017. Lecture Notes in Computer Science(), vol 10544. Springer, Cham. https://doi.org/10.1007/978-3-319-74433-9_17
Download citation
DOI: https://doi.org/10.1007/978-3-319-74433-9_17
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-74432-2
Online ISBN: 978-3-319-74433-9
eBook Packages: Computer ScienceComputer Science (R0)