Crowdsourcing the character of a place: Character-level convolutional networks for multilingual geographic text classification

Transactions in GIS

Volumn 22, Issue 2, 2018, Pages 394-408

  Adams, Benjamin ^a   McKenzie, Grant ^b  

^a UNIVERSITY OF CANTERBURY   (New Zealand)

^b UNIVERSITY OF MARYLAND   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

CLASSIFICATION (OF INFORMATION); CLEANING; CONVOLUTION; CROWDSOURCING; OPTICAL CHARACTER RECOGNITION; SOCIAL NETWORKING (ONLINE); TEXT PROCESSING;

CHARACTER ENCODING; CHARACTER LEVEL; CONVOLUTIONAL NETWORKS; CONVOLUTIONAL NEURAL NETWORK; GEOGRAPHICS; MULTILINGUAL TEXTS; NEURAL NETWORK MODEL; STATE OF THE ART; TEXT CLASSIFICATION; TEXT CLASSIFICATION METHODS;

NEURAL NETWORK MODELS;

ALGORITHM; ARTIFICIAL NEURAL NETWORK; CROWDSOURCING; INTERNET; SOCIAL MEDIA;

EID: 85041029948     PISSN: 13611682     EISSN: 14679671     Source Type: Journal    
DOI: 10.1111/tgis.12317     Document Type: Article

References (66)

1. Adams, B. (2017). Wãhi, a discrete global grid gazetteer built using linked open data. International Journal of Digital Earth, 10, 490–503.
2. Adams, B., & Gahegan, M. (2016). Exploratory chronotopic data analysis. In J. A. Miller, D. O'Sullivan, & N. Wiegand (Eds.), Lecture Notes in Computer Science: Vol. 9927. Geographic Information Science: Proceedings of the Ninth International Conference, GIScience 2016 (pp. 243–258), Montreal, QC, Canada, September 27–30. Berlin, Germany: Springer.
3. Adams, B., & Janowicz, K. (2012). On the geo-indicativeness of non-georeferenced text. In Proceedings of the Sixth International AAAI Conference on Weblogs and Social Media (pp. 375–378). Dublin, Ireland: AAAI.
4. Adams, B., McKenzie, G., & Gahegan, M. (2015). Frankenplace: Interactive thematic mapping for ad hoc exploratory search. In Proceedings of the 24th International Conference on World Wide Web (pp. 12–22). Florence, Italy: ACM.
5. Amitay, E., Har'El, N., Sivan, R., & Soffer, A. (2004). Web-a-where: Geotagging web content. In Proceedings of the 27th International ACM SIGIR Conference on Research and Development in Information Retrieval (pp. 273–280). Sheffield, UK: ACM.
6. Androutsopoulos, I., Koutsias, J., Chandrinos, K. V., & Spyropoulos, C. D. (2000). An experimental comparison of naive Bayesian and keyword-based anti-spam filtering with personal e-mail messages. In Proceedings of the 23rd International ACM SIGIR Conference on Research and Development in Information Retrieval (pp. 160–167). Athens, Greece: ACM.
7. Bizer, C., Lehmann, J., Kobilarov, G., Auer, S., Becker, C., Cyganiak, R., & Hellmann, S. (2009). DBpedia: A crystallization point for the web of data. Web Semantics: Science, Services & Agents on the World Wide Web, 7, 154–165.
8. Boyd, D., & Crawford, K. (2012). Critical questions for big data: Provocations for a cultural, technological, and scholarly phenomenon. Information, Communication & Society, 15, 662–679.
9. Burges, C. J. (1998). A tutorial on support vector machines for pattern recognition. Data Mining & Knowledge Discovery, 2, 121–167.
10. Cheng, G., Zhou, P., & Han, J. (2016). Learning rotation-invariant convolutional neural networks for object detection in VHR optical remote sensing images. IEEE Transactions on Geoscience and Remote Sensing, 54, 7405–7415.
11. Cheng, Z., Caverlee, J., & Lee, K. (2010). You are where you tweet: A content-based approach to geo-locating Twitter users. In Proceedings of the 19th ACM International Conference on Information and Knowledge Management (pp. 759–768). Toronto, Canada: ACM.
12. Clough, P. (2005). Extracting metadata for spatially-aware information retrieval on the internet. In Proceedings of the Second International Workshop on Geographic Information Retrieval (pp. 25–30). Bremen, Germany: ACM.
13. Collobert, R., Kavukcuoglu, K., & Farabet, C. (2011). Torch7: A Matlab-like environment for machine learning. In Proceedings of the Big Learning: Algorithms, Systems, and Tools for Learning at Scale NIPS Workshop. Sierra Nevada, Spain.
14. Cortes, C., & Vapnik, V. (1995). Support-vector networks. Machine Learning, 20, 273–297.
15. DeLozier, G., Baldridge, J., & London, L. (2015). Gazetteer-independent toponym resolution using geographic word profiles. In Proceedings of the 29th AAAI Conference on Artificial Intelligence (pp. 2382–2388). Austin, TX: AAAI.
16. Eisenstein, J., O'Connor, B., Smith, N. A., & Xing, E. P. (2010). A latent variable model for geographic lexical variation. In Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing (pp. 1277–1287). Cambridge, MA: SIGDAT.
17. Gelernter, J., & Mushegian, N. (2011). Geo-parsing messages from microtext. Transactions in GIS, 15, 753–773.
18. Girshick, R., Donahue, J., Darrell, T., & Malik, J. (2014). Rich feature hierarchies for accurate object detection and semantic segmentation. In Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition (pp. 580–587). Columbus, OH: IEEE.
19. Goodchild, M. F. (2007). Citizens as sensors: The world of volunteered geography. GeoJournal, 69, 211–221.
20. Han, B., & Baldwin, T. (2011). Lexical normalisation of short text messages: Makn sens a# twitter. In Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies (Vol. 1, pp. 368–378). Portland, OR: ACM.
21. Han, B., Cook, P., & Baldwin, T. (2014). Text-based Twitter user geolocation prediction. Journal of Artificial Intelligence Research, 49, 451–500.
22. Hao, Q., Cai, R., Wang, C., Xiao, R., Yang, J.-M., Pang, Y., & Zhang, L. (2010). Equip tourists with knowledge mined from travelogues. In Proceedings of the 19th International Conference on World Wide Web (pp. 401–410). Raleigh, NC: ACM.
23. Hays, J., & Efros, A. A. (2008). Im2gps: Estimating geographic information from a single image. In Proceedings of the 26th IEEE Conference on Computer Vision and Pattern Recognition. Anchorage, AK: IEEE.
24. Hovy, D., Johannsen, A., & Søgaard, A. (2015). User review sites as a resource for large-scale sociolinguistic studies. In Proceedings of the 24th International Conference on World Wide Web (pp. 452–461). Florence, Italy: ACM.
25. Joachims, T. (1998). Text categorization with support vector machines: Learning with many relevant features. In Proceedings of the 10th European Conference on Machine Learning (pp. 137–142). Chemnitz, Germany.
26. Jones, C. B., & Purves, R. S. (2008). Geographical information retrieval. International Journal of Geographical Information Science, 22, 219–228.
27. Jones, C. B., Purves, R., Ruas, A., Sanderson, M., Sester, M., Van Kreveld, M., & Weibel, R. (2002). Spatial information retrieval and geographical ontologies. An overview of the SPIRIT project. In Proceedings of the 25th International ACM SIGIR Conference on Research and Development in Information Retrieval (pp. 387–388). Tampere, Finland: ACM.
28. Ju, Y., Adams, B., Janowicz, K., Hu, Y., Yan, B., & McKenzie, G. (2016). Things and strings: Improving place name disambiguation from short texts by combining entity co-occurrence with topic modeling. In Proceedings of the 20th International Conference on Knowledge Engineering and Knowledge Management (pp. 353–367). Bologna, Italy.
29. Kim, Y. (2014). Convolutional neural networks for sentence classification. In Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (pp. 1746–1751). Doha, Qatar.
30. Kim, Y., Jernite, Y., Sontag, D., & Rush, A. M. (2016). Character-aware neural language models. In Proceedings of the 30th AAAI Conference on Artificial Intelligence (pp. 2741–2749). Phoenix, AZ: AAAI.
31. Kolak, O., & Resnik, P. (2002). OCR error correction using a noisy channel model. In Proceedings of the Second International Conference on Human Language Technology Research (pp. 257–262). San Diego, CA: ACM.
32. Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). ImageNet classification with deep convolutional neural networks. In Proceedings of the 25th International Conference on Neural Information Processing Systems (Vol. 1, pp. 1097–1105). Lake Tahoe, NV: ACM.
33. Laere, O. V., Schockaert, S., Tanasescu, V., Dhoedt, B., & Jones, C. B. (2014). Georeferencing Wikipedia documents using data from social media sources. ACM Transactions on Information Systems, 32, 12.
34. Lample, G., Ballesteros, M., Subramanian, S., Kawakami, K., & Dyer, C. (2016). Neural architectures for named entity recognition. In Proceedings of the 15th Annual Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (pp. 260–270). San Diego, CA: NAACL.
35. LeCun, Y., Boser, B., Denker, J. S., Henderson, D., Howard, R. E., Hubbard, W., & Jackel, L. D. (1989). Backpropagation applied to handwritten ZIP code recognition. Neural Computation, 1, 541–551.
36. LeCun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998). Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86, 2278–2324.
37. Lee, R., & Sumiya, K. (2010). Measuring geographical regularities of crowd behaviors for Twitter-based geo-social event detection. In Proceedings of the Second ACM SIGSPATIAL International Workshop on Location Based Social Networks (pp. 1–10). San Jose, CA: ACM.
38. Liu, J., & Inkpen, D. (2015). Estimating user location in social media with stacked denoising auto-encoders. In Proceedings of the 14th Annual Conference of the North American Chapter of the Association for Computational Linguistics: Human Language (pp. 201–210). Denver, CO: NAACL.
39. Maggiori, E., Tarabalka, Y., Charpiat, G., & Alliez, P. (2017). Convolutional neural networks for large-scale remote-sensing image classification. IEEE Transactions on Geoscience & Remote Sensing, 55, 645–657.
40. McCallum, A., & Nigam, K. (1998). A comparison of event models for naïve Bayes text classification. In Proceedings of the AAAI-98 Workshop on Learning for Text Categorization (pp. 41–48). Madison, WI: AAAI.
41. Melo, F., & Martins, B. (2017). Automated geocoding of textual documents: A survey of current approaches. Transactions in GIS, 21, 3–38.
42. Mitchell, L., Frank, M. R., Harris, K. D., Dodds, P. S., & Danforth, C. M. (2013). The geography of happiness: Connecting Twitter sentiment and expression, demographics, and objective characteristics of place. PLoS One, 8, e64417.
43. Monteiro, B. R., Davis, C. A., & Fonseca, F. (2016). A survey on the geographic scope of textual documents. Computers & Geosciences, 96, 23–34.
44. Nogueira, K., Penatti, O. A., & dos Santos, J. A. (2017). Towards better exploiting convolutional neural networks for remote sensing scene classification. Pattern Recognition, 61, 539–556.
45. Overell, S., & Rüger, S. (2008). Using co-occurrence models for placename disambiguation. International Journal of Geographical Information Science, 22, 265–287.
46. Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1988). Learning representations by back-propagating errors. Cognitive Modeling, 5, 1.
47. Sakaki, T., Okazaki, M., & Matsuo, Y. (2010). Earthquake shakes Twitter users: Real-time event detection by social sensors. In Proceedings of the 19th International Conference on World Wide Web (pp. 851–860). Raleigh, NC.
48. Salton, G., Wong, A., & Yang, C.-S. (1975). A vector space model for automatic indexing. Communications of the ACM, 18, 613–620.
49. Schmidhuber, J. (2015). Deep learning in neural networks: An overview. Neural Networks, 61, 85–117.
50. Schwartz, H. A., Eichstaedt, J. C., Kern, M. L., Dziurzynski, L., Lucas, R. E., Agrawal, M., … Ungar, L. (2013). Characterizing geographic variation in well-being using tweets. In Proceedings of the Seventh International AAAI Conference on Weblogs and Social Media (pp. 583–591). Cambridge, MA: AAAI.
51. Scott, S., & Matwin, S. (1999). Feature engineering for text classification. In I. Bratko & S. Dzeroski (Eds.), Proceedings of ICML-99, 16th International Conference on Machine Learning (pp. 379–388). San Francisco, CA: Morgan Kaufmann.
52. Sebastiani, F. (2002). Machine learning in automated text categorization. ACM Computing Surveys, 34(1), 1–47.
53. Silva, M. J., Martins, B., Chaves, M., Afonso, A. P., & Cardoso, N. (2006). Adding geographic scopes to web resources. Computers, Environment & Urban Systems, 30, 378–399.
54. Snyder, J. P. (1992). An equal-area map projection for polyhedral globes. Cartographica, 29(1), 10–21.
55. Srivastava, N., Hinton, G. E., Krizhevsky, A., Sutskever, I., & Salakhutdinov, R. (2014). Dropout: A simple way to prevent neural networks from overfitting. Journal of Machine Learning Research, 15(1), 1929–1958.
56. Subramaniam, L. V., Roy, S., Faruquie, T. A., & Negi, S. (2009). A survey of types of text noise and techniques to handle noisy text. In Proceedings of the Third Workshop on Analytics for Noisy Unstructured Text Data (pp. 115–122). Barcelona, Spain: ACM.
57. Sui, D., & Goodchild, M. F. (2011). The convergence of GIS and social media: Challenges for GIScience. International Journal of Geographical Information Science, 25, 1737–1748.
58. Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., … Rabinovich, A. (2015). Going deeper with convolutions. In Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition. Boston, MA: IEEE.
59. Wehrmann, J., Becker, W., Cagnini, H. E., & Barros, R. C. (2017). A character-based convolutional neural network for language-agnostic Twitter sentiment analysis. In Proceedings of the 2017 International Joint Conference on Neural Networks (pp. 2384–2391). Anchorage, AK: IEEE.
60. Wing, B. P., & Baldridge, J. (2011). Simple supervised document geolocation with geodesic grids. In Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies (Vol. 1, pp. 955–964). Portland, OR: ACM.
61. Yergeau, F. (2003). UTF-8: A transformation format of ISO 10646. Retrieved from https://tools.ietf.org/html/rfc3629
62. Yi, X., Raghavan, H., & Leggetter, C. (2009). Discovering users’ specific geo intention in web search. In Proceedings of the 18th International Conference on World Wide Web (pp. 481–490). Madrid, Spain: ACM.
63. Yin, J., Lampert, A., Cameron, M., Robinson, B., & Power, R. (2012). Using social media to enhance emergency situation awareness. IEEE Intelligent Systems, 27, 52–59.
64. Zeiler, M. D. (2012). ADADELTA: An adaptive learning rate method. https://arxiv.org/abs/1212.5701
65. Zhang, W., & Gelernter, J. (2014). Geocoding location expressions in Twitter messages: A preference learning method. Journal of Spatial Information Science, 2014, 37–70.
66. Zhang, X., Zhao, J., & LeCun, Y. (2015). Character-level convolutional networks for text classification. In Proceedings of the 29th Annual Conference on Neural Information Processing Systems (pp. 649–657). Montreal, Quebec: NIPS.