Location Name Disambiguation Exploiting Spatial Proximity and Temporal Consistency

Takashi Awamura,Tomohide Shibata,Eiji Aramaki,Daisuke Kawahara,Sadao Kurohashi

doi:10.3115/v1/w15-1701

Abstract

As the volume of documents on the Web increases, technologies to extract useful information from them become increasingly essential. For instance, information extracted from social network services such as Twitter and Facebook is useful because it contains a lot of location-specific information. To extract such information, it is necessary to identify the location of each location-relevant expression within a document. Previous studies on location disambiguation have tackled this problem on the basis of word sense disambiguation, and did not make use of location-specific clues. In this paper, we propose a method for location disambiguation that takes advantage of the following two clues: spatial proximity and temporal consistency. We confirm the effectiveness of these clues through experiments on Twitter tweets with GPS information.

Highlights

As the volume of documents on the Web increases, technologies to extract useful information from them become increasingly essential
We propose a method that identifies the locations of location expressions in Twitter tweets on the basis of the following two clues: (1) spatial proximity, and (2) temporal consistency
We focus on Location EXpression (LEX) and Location Entity (LE) that have geographic information system (GIS) information on Wikipedia

Summary

Introduction

As the volume of documents on the Web increases, technologies to extract useful information from them become increasingly essential. Information extracted from social network services (SNS) such as Twitter and Facebook is useful because it contains a lot of location-specific information. To extract such information, it is necessary to identify the location of each location-relevant expression within a document. Previous studies on location disambiguation made use of methods for word sense disambiguation and are based only on textual information, i.e., the bagof-words in a document. It is, difficult to solve this problem using only textual information in a relatively short SNS document. It is difficult to identify the location of “Prefectural Office Ave.” from the following document based only on word information.

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