Leveraging Spatial Community Information in Location Recognition in Tweets
Article No.: 4, Pages 1 - 9
Abstract
Location names are very helpful in event extraction. Informal social texts pose significant challenges for recognizing location names. However, social texts have an advantage that can be leveraged: spatial and social network contexts. We address the location recognizing task as a part of named entity recognition, and introduce a new approach which leverages community contexts and captures language variations among groups of users. Specifically, we incorporate a community component into a topic modeling method and harness unlabeled tweets. Experiments on a large Twitter dataset show that our proposed method can improve the location classification F1 score by 5%.
References
[1]
Einat Amitay, Nadav Har'El, Ron Sivan, and Aya Soffer. 2004. Web-a-where: geotagging web content. In Proceedings of the 27th ACM SIGIR. 273--280.
[2]
Timothy Baldwin, Young-Bum Kim, Marie Catherine de Marneffe, Alan Ritter, Bo Han, and Wei Xu. 2015. Shared tasks of the 2015 workshop on noisy user-generated text: Twitter lexical normalization and named entity recognition. (2015), 126.
[3]
David M. Blei, Andrew Y. Ng, and Michael I. Jordan. 2003. Latent dirichlet allocation. The Journal of machine Learning research 3 (2003), 993--1022.
[4]
Peter F Brown, Peter V Desouza, Robert L Mercer, Vincent J Della Pietra, and Jenifer C Lai. 1992. Class-based n-gram models of natural language. Computational linguistics 18, 4 (1992), 467--479.
[5]
Hal DaumÃl' III. 2008. hbc: Hierarchical bayes compiler. Pre-release version 0.7, URL http://www.cs.utah.edu/~hal/HBC (2008).
[6]
Leon Derczynski, Diana Maynard, Giuseppe Rizzo, Marieke van Erp, Genevieve Gorrell, RaphaÃńl Troncy, Johann Petrak, and Kalina Bontcheva. 2015. Analysis of named entity recognition and linking for tweets. 51, 2 (2015), 32--49. https://doi.org/10.1016/j.ipm.2014.10.006
[7]
Jacob Eisenstein, Brendan O'Connor, Noah A. Smith, and Eric P. Xing. 2010. A latent variable model for geographic lexical variation. In Proceedings of the 2010 EMNLP. ACL, 1277--1287.
[8]
Tim Finin, Will Murnane, Anand Karandikar, Nicholas Keller, Justin Martineau, and Mark Dredze. 2010. Annotating named entities in Twitter data with crowd-sourcing. In Proceedings of the NAACL HLT 2010 Workshop on Creating Speech and Language Data with Amazon's Mechanical Turk. ACL, 80--88.
[9]
Jenny Rose Finkel, Trond Grenager, and Christopher Manning. 2005. Incorporating non-local information into information extraction systems by gibbs sampling. In Proceedings of the 43rd Annual Meeting on ACL. 363--370.
[10]
Thomas L. Griffiths and Mark Steyvers. 2004. Finding scientific topics. Proceedings of the National Academy of Sciences 101 (2004), 5228--5235. Issue suppl 1.
[11]
Liangjie Hong, Amr Ahmed, Siva Gurumurthy, Alexander J. Smola, and Kostas Tsioutsiouliklis. 2012. Discovering Geographical Topics in the Twitter Stream. In Proceedings of the 21st International Conference on World Wide Web (WWW '12). ACM, New York, NY, USA, 769--778.
[12]
Vijay Krishnan and Christopher D. Manning. 2006. An Effective Two-stage Model for Exploiting Non-local Dependencies in Named Entity Recognition. In Proceedings of the 21st COLING and the 44th Annual Meeting of the ACL. ACL, 1121--1128.
[13]
John Lafferty, Andrew McCallum, and Fernando CN Pereira. 2001. Conditional random fields: Probabilistic models for segmenting and labeling sequence data. (2001).
[14]
Rui Li, Shengjie Wang, Hongbo Deng, Rui Wang, and Kevin Chen-Chuan Chang. 2012. Towards social user profiling: unified and discriminative influence model for inferring home locations. In KDD. 1023--1031.
[15]
Xiaohua Liu, Shaodian Zhang, Furu Wei, and Ming Zhou. 2011. Recognizing Named Entities in Tweets. 359--367.
[16]
Xiaohua Liu, Ming Zhou, Furu Wei, Zhongyang Fu, and Xiangyang Zhou. 2012. Joint inference of named entity recognition and normalization for tweets. In Proceedings of the 50th Annual Meeting of the ACL: Long Papers-Volume 1. ACL, 526--535.
[17]
Michael Paul and Roxana Girju. 2009. Cross-cultural Analysis of Blogs and Forums with Mixed-collection Topic Models. In Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 3 - Volume 3 (EMNLP '09). Association for Computational Linguistics, Stroudsburg, PA, USA, 1408--1417.
[18]
Daniel Ramage, David Hall, Ramesh Nallapati, and Christopher D. Manning. 2009. Labeled LDA: A supervised topic model for credit attribution in multi-labeled corpora. In Proceedings of the 2009 EMNLP: Volume 1-Volume 1. ACL, 248--256.
[19]
Lev Ratinov and Dan Roth. 2009. Design challenges and misconceptions in named entity recognition. In Proceedings of the Thirteenth CoNLL. 147--155.
[20]
Alan Ritter, Sam Clark, and Oren Etzioni. 2011. Named entity recognition in tweets: an experimental study. In EMNLP. ACL, 1524--1534.
[21]
Jagan Sankaranarayanan, Hanan Samet, Benjamin E. Teitler, Michael D. Lieberman, and Jon Sperling. 2009. TwitterStand: News in Tweets. In Proceedings of the 17th ACM SIGSPATIAL (GIS '09). ACM, New York, NY, USA, 42--51.
[22]
Fei Sha and Fernando Pereira. 2003. Shallow parsing with conditional random fields. In Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology-Volume 1. Association for Computational Linguistics, 134--141.
[23]
David A. Smith and Gregory Crane. 2001. Disambiguating Geographic Names in a Historical Digital Library. In Proceedings of the 5th ECDL. Springer-Verlag, 127--136.
[24]
Ikuya Yamada, Hideaki Takeda, and Yoshiyasu Takefuji. 2015. Enhancing Named Entity Recognition in Twitter Messages Using Entity Linking. (2015), 136.
[25]
Limin Yao, David Mimno, and Andrew McCallum. 2009. Efficient methods for topic model inference on streaming document collections. In Proceedings of the 15th ACM SIGKDD. ACM, 937--946.
[26]
Zhijun Yin, Liangliang Cao, Jiawei Han, Chengxiang Zhai, and Thomas Huang. 2011. Geographical Topic Discovery and Comparison. In Proceedings of the 20th International Conference on World Wide Web (WWW '11). ACM, New York, NY, USA, 247--256.
- Leveraging Spatial Community Information in Location Recognition in Tweets
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November 2017
50 pages
ISBN:9781450355001
DOI:10.1145/3148044
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Published: 07 November 2017
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SIGSPATIAL'17: 25th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
November 7, 2017
CA, Redondo Beach, USA
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