Article | Proceedings of the 20th Nordic Conference of Computational Linguistics, NODALIDA 2015, May 11-13, 2015, Vilnius, Lithuania | Inferring the location of authors from words in their texts Link�ping University Electronic Press Conference Proceedings
Göm menyn

Title:
Inferring the location of authors from words in their texts
Author:
Max Berggren: Gavagai & Royal Institute of Technology, KTH, Stockholm, Sweden Jussi Karlgren: Gavagai & Royal Institute of Technology, KTH, Stockholm, Sweden Robert Östling: Department of Linguistics, Stockholm University, Sweden Mikael Parkval: Department of Linguistics, Stockholm University, Sweden
Download:
Full text (pdf)
Year:
2015
Conference:
Proceedings of the 20th Nordic Conference of Computational Linguistics, NODALIDA 2015, May 11-13, 2015, Vilnius, Lithuania
Issue:
109
Article no.:
026
Pages:
211-218
No. of pages:
8
Publication type:
Abstract and Fulltext
Published:
2015-05-06
ISBN:
978-91-7519-098-3
Series:
Linköping Electronic Conference Proceedings
ISSN (print):
1650-3686
ISSN (online):
1650-3740
Series:
NEALT Proceedings Series
Publisher:
Linköping University Electronic Press, Linköpings universitet


Export in BibTex, RIS or text

For the purposes of computational dialectology or other geographically bound text analysis tasks, texts must be annotated with their or their authors’ location. Many texts are locatable but most have no explicit annotation of place. This paper describes a series of experiments to determine how positionally annotated microblog posts can be used to learn location indicating words which then can be used to locate. From previous research efforts, a Gaussian distribution is used to model the locational qualities of words. We introduce the notion of placeness to describe how locational words are. We find that modelling word distributions to account for several locations and thus several Gaussian distributions per word, defining a filter which picks out words with high placeness based on their local distributional context, and aggregating locational information in a centroid gives the most useful results. The results are applied to data in the Swedish language.

Proceedings of the 20th Nordic Conference of Computational Linguistics, NODALIDA 2015, May 11-13, 2015, Vilnius, Lithuania

Author:
Max Berggren, Jussi Karlgren, Robert Östling, Mikael Parkval
Title:
Inferring the location of authors from words in their texts
References:

Lars Backstrom, Jon Kleinberg, Ravi Kumar, and Jasmine Novak. Spatial variation in search engine queries. In 17th international conference on World Wide Web. ACM, 2008.

Zhiyyan Cheng, James Caverlee, and Kyumin Lee. You are where you tweet: a content-based approach to geolocating Twitter users. In 19th ACM international Conference on Information and Knowledge Management. ACM, 2010.

Jacob Eisenstein, Brendan O’Connor, Noah A Smith, and Eric P Xing. A latent variable model for geographic lexical variation. In Conference on Empirical Methods in Natural Language Processing. ACL, 2010.

Bo Han, Paul Cook, and Timothy Baldwin. Text-based Twitter user geolocation prediction. Journal of Artificial Intelligence Research (JAIR), 49:451–500, 2014.

Liangjie Hong, Amr Ahmed, Siva Gurumurthy, Alexander J Smola, and Kostas Tsioutsiouliklis. Discovering geographical topics in the Twitter stream. In 21st international conference on World Wide Web. ACM, 2012.

Sheila Kinsella, Vanessa Murdock, and Neil O’Hare. I’m eating a sandwich in Glasgow: modeling locations with tweets. In 3rd international workshop on Search and mining user-generated contents. ACM, 2011.

Guoliang Li, Jun Hu, Jianhua Feng, and Kian-lee Tan. Effective location identification from microblogs. In 30th IEEE International Conference on Data Engineering. IEEE, 2014.

Jalal Mahmud, Jeffrey Nichols, and Clemens Drews. Where is this tweet from? Inferring home locations of Twitter users. In 6th International AAAI Conference on Web and Social Media, 2012.

Mikael Parkvall. H¨ar g°ar gr¨ansen. Spr°aktidningen, October 2012. ISSN 1654-5028.

Reid Priedhorsky, Aron Culotta, and Sara Y Del Valle. Inferring the origin locations of tweets with quantitative confidence. In 17th ACM conference on Computer Supported Cooperative Work & Social Computing. ACM, 2014.

Zhijun Yin, Liangliang Cao, Jiawei Han, Chengxiang Zhai, and Thomas Huang. Geographical topic discovery and comparison. In 20th international conference on World Wide Web. ACM, 2011.

Proceedings of the 20th Nordic Conference of Computational Linguistics, NODALIDA 2015, May 11-13, 2015, Vilnius, Lithuania

Author:
Max Berggren, Jussi Karlgren, Robert Östling, Mikael Parkval
Title:
Inferring the location of authors from words in their texts
Note: the following are taken directly from CrossRef
Citations:
No citations available at the moment


Responsible for this page: Peter Berkesand
Last updated: 2018-9-11