A Geo-referencing Places from Everyday Natural Language Place Descriptions Hao Chen, University of Melbourne Maria Vasardani, University of Melbourne Stephan Winter, University of Melbourne Natural language place descriptions in everyday communication provide a rich source of spatial knowledge about places. An important step to utilize such knowledge in information systems is geo-referencing all the places referred to in these descriptions. Current techniques for geo-referencing places from text documents are using place name recognition and disambiguation; however, place descriptions often contain place ref- erences that are not known by gazetteers, or that are expressed in other, more flexible ways. Hence, the approach for geo-referencing presented in this paper starts from a place graph that contains the place ref- erences as well as spatial relationships extracted from place descriptions. Spatial relationships are used to constrain the locations of places and allow the later best-matching process for geo-referencing. The novel geo-referencing process results in higher precision and recall compared to state-of-art toponym resolution approaches on several tested place description datasets. CCS Concepts: •Information systems → Geographic information systems; Additional Key Words and Phrases: Natural language place description, toponym resolution, qualitative spatial relationship ACM Reference Format: Hao Chen, Maria Vasardani, and Stephan Winter, 2016. Geo-referencing Places from Everyday Natural Language Place Descriptions. ACM Trans. Spatial Algorithms Syst. V, N, Article A (January YYYY), 29 pages. DOI: http://dx.doi.org/10.1145/0000000.0000000 1. INTRODUCTION With the increasing volume of unstructured text documents being published online, as well as the growing need for place-related information in everyday life, the relation between places and text documents has recently attracted research attention, and the necessity of identifying and locating places from text documents has been emphasized by others [Jones et al. 2001; Schlieder et al. 2001; Hill 2006; Teitler et al. 2008]. Place information extracted from text can be used to facilitate a wide range of applications such as geographic information retrieval [Silva et al. 2006; Purves et al. 2007; Jones and Purves 2008], to smooth human-computer interaction [Raubal 2009; Winter et al. 2016; Davies et al. 2009], and to build place information systems. The rapid develop- ment of text mining and natural language processing techniques makes it feasible to extract information from text documents, through information extraction techniques such as named entity recognition and relation extraction. This research focuses on natural language place descriptions as data input. Natu- ral language place descriptions occur in everyday verbal communication as a way of encoding and transmitting spatial knowledge about place between individuals [Vasar- Author’s addresses: H. Chen, M. Vasardani, and S. Winter, Department of Infrastructure Engineering, Uni- versity of Melbourne, Parkville, Victoria 3010, Australia. Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or repub- lish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from permissions@acm.org. c  YYYY ACM. 2374-0353/YYYY/01-ARTA $15.00 DOI: http://dx.doi.org/10.1145/0000000.0000000 ACM Transactions on Spatial Algorithms and Systems, Vol. V, No. N, Article A, Publication date: January YYYY. arXiv:1710.03346v1 [cs.AI] 9 Oct 2017