On assigning place names to geography related web pages

Zong, Wenbo; Wu, Dan; Sun, Aixin; Lim, Ee-Peng; Goh, Dion Hoe‐Lian

doi:10.1145/1065385.1065464

Cited by 59 publications

(39 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…For example, the tagging of both user queries (Wang, Wang, Xie, Forman, Lu, Ma, & Li, 2005;Backstrom, Kleinberg, Kumar, & Novak, 2008;Yi, Raghavan, & Leggetter, 2009) and web pages (Ding, Gravano, & Shivakumar, 2000;Amitay, Har'El, Sivan, & Soffer, 2004;Zong, Wu, Sun, Lim, & Goh, 2005;Silva, Martins, Chaves, Afonso, & Cardoso, 2006;Bennett, Radlinski, White, & Yilmaz, 2011) has been considered in information retrieval. In geographical information science, the primary focus has been on recognising location mentions in text (Leidner & Lieberman, 2011), with named entity recognition tools typically employed to detect and extract such mentions (Quercini, Samet, Sankaranarayanan, & Lieberman, 2010;Gelernter & Mushegian, 2011).…”

Section: Related Workmentioning

confidence: 99%

Text-Based Twitter User Geolocation Prediction

Han¹,

Cook²,

Baldwin³

2014

jair

254

289

View full text Add to dashboard Cite

Geographical location is vital to geospatial applications like local search and event detection. In this paper, we investigate and improve on the task of text-based geolocation prediction of Twitter users. Previous studies on this topic have typically assumed that geographical references (e.g., gazetteer terms, dialectal words) in a text are indicative of its author's location. However, these references are often buried in informal, ungrammatical, and multilingual data, and are therefore non-trivial to identify and exploit. We present an integrated geolocation prediction framework and investigate what factors impact on prediction accuracy. First, we evaluate a range of feature selection methods to obtain "location indicative words". We then evaluate the impact of nongeotagged tweets, language, and user-declared metadata on geolocation prediction. In addition, we evaluate the impact of temporal variance on model generalisation, and discuss how users differ in terms of their geolocatability.We achieve state-of-the-art results for the text-based Twitter user geolocation task, and also provide the most extensive exploration of the task to date. Our findings provide valuable insights into the design of robust, practical text-based geolocation prediction systems.

show abstract

Section: Related Workmentioning

confidence: 99%

Text-Based Twitter User Geolocation Prediction

Han¹,

Cook²,

Baldwin³

2014

jair

254

289

View full text Add to dashboard Cite

show abstract

“…This information can be exploited and used to provide spatial awareness to information systems (see, e.g., Buckland et al, 2007;Purves et al, 2007)). Zong et al (2005) discuss how the ability to perform query by location can be an important and useful addition to a digital library and Buckland et al (2007, p. 376) concur with this in saying "libraries have a broad need to support geographic search". This paper presents an overview and case study of the challenges and goals of linking archival data to location.…”

Section: Introductionmentioning

confidence: 99%

Linking archival data to location: a case study at the UK National Archives

Clough

Tang

Hall

et al. 2011

View full text Add to dashboard Cite

Purpose -The National Archives (TNA) is the UK government's official archive. It stores and maintains records spanning over a thousand years in both physical and digital form. Much of the information held by TNA includes references to place and frequently user queries to TNA's online catalogue involve searches for location. The purpose of this paper is to illustrate how TNA have extracted the geographic references in their historic data to improve access to the archives. Approach -To be able to quickly enhance the existing archival data with geographic information, existing technologies from Natural Language Processing (NLP) and Geographical Information Retrieval (GIR) have been utilised and adapted to historical archives. Findings -Enhancing the archival records with geographic information has enabled TNA to quickly develop a number of case-studies highlighting how geographic information can improve access to large-scale archival collections. The use of existing methods from the GIR domain and technologies, such as OpenLayers, enabled us to quickly implement this process in a way that is easily transferrable to other institutions. Practical implications -The methods and technologies described in this paper can be adapted by other archives to similarly enhance access to their historic data. Also the data-sharing methods described can be used to enable the integration of knowledge held at different archival institutions. Value -Place is one of the core dimensions for TNA's archival data. Many of the records that we hold make reference to place data (wills, legislation, court cases…) and approximately one fifth of users' searches involve place names. However, there are still a number of open questions regarding the adaptation of existing GIR methods to the history domain. This paper presents an overview over available GIR methods and the challenges in applying them to historical data.

show abstract

“…Our research targeted the so-called context locations, defined as "the geographic location that the content of a web [18]. While related to the work done by Martins et al in [10], we apply an SVM for the initial NER task, and only use the geocoder/gazetter for lookups after the initial candidate geospatial NEs have been identified.…”

Section: Geospatial Named Entity Resolutionmentioning

confidence: 99%

Extracting Geospatial Entities from Wikipedia

Witmer

Kalita

2009

2009 IEEE International Conference on Semantic Computing

View full text Add to dashboard Cite

This paper addresses the challenge of extracting geospatial data from the article text of the English Wikipedia. In the first phase of our work, we create a training corpus and select a set of word-based features to train a Support Vector Machine (SVM) for the task of geospatial named entity recognition. We target for testing a corpus of Wikipedia articles about battles and wars, as these have a high incidence of geospatial content. The SVM recognizes place names in the corpus with a very high recall, close to 100%, with an acceptable precision. The set of geospatial NEs is then fed into a geocoding and resolution process, whose goal is to determine the correct coordinates for each place name. As many place names are ambiguous, and do not immediately geocode to a single location, we present a data structure and algorithm to resolve ambiguity based on sentence and article context, so the correct coordinates can be selected. We achieve an f-measure of 82%, and create a set of geospatial entities for each article, combining the place names, spatial locations, and an assumed point geometry. These entities can enable geospatial search on and geovisualization of Wikipedia.

show abstract

On assigning place names to geography related web pages

Cited by 59 publications

References 11 publications

Text-Based Twitter User Geolocation Prediction

Text-Based Twitter User Geolocation Prediction

Linking archival data to location: a case study at the UK National Archives

Extracting Geospatial Entities from Wikipedia

Contact Info

Product

Resources

About