During the last few years, the amount of online descriptive information about places has reached reasonable dimensions for many cities in the world. Being such information mostly in Natural Language text, Information Extraction techniques are needed for obtaining the meaning of places that underlies these massive amounts of commonsense and user made sources. In this article, we show how we automatically label places using Information Extraction techniques applied to online resources such as Wikipedia, Yellow Pages and Yahoo.
Keywords
Information Extraction, Ambient Intelligence, Semantics of Place
Subject
Ubiquitous Computing
Conference
First International Joint Conference on Ambient Intelligence (acc. rate. 38.5%), November 2010
DOI
Cited by
Year 2016 : 1 citations
Martinkus, Phil, and M. S. C. S. Praveen Madiraju. "Personalizing Places of Interest Using Social Media Analysis." Poster in CATA 2016.
Year 2015 : 1 citations
Onur Ekmekci, Andres Sevtsuk. 50 ways to Singapore Rail Corridor. Project at MIT. http://cityform.mit.edu/projects/50
Year 2014 : 2 citations
Eunyoung Kim, Hwon Ihm, and Sung-Hyon Myaeng. 2014. Topic-based place semantics discovered from microblogging text messages. In Proceedings of the companion publication of the 23rd international conference on World wide web companion (WWW Companion '14). International World Wide Web Conferences Steering Committee, Republic and Canton of Geneva, Switzerland, 561-562. DOI=10.1145/2567948.2576955
Kim, E., Ihm, H., & Myaeng, S. H. (2014, April). Topic-based place semantics discovered from microblogging text messages. In Proceedings of the 23rd International Conference on World Wide Web (pp. 561-562). ACM.
