Geotagging with local lexicons to build indexes for textually-specified spatial data

Lieberman, Michael D.; Samet, Hanan; Sankaranarayanan, Jagan

doi:10.1109/icde.2010.5447903

Cited by 122 publications

(124 citation statements)

References 16 publications

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“…As mentioned in the previous section, there are several studies for the task. The majority of these studies models the location inference as a multi-class classification problem on the grid over geo-spatial areas or cities on the gazetteer such as GeoNames and DBpedia [12], [16].…”

Section: Pilot Categorization: Why Do We Focus Onmentioning

confidence: 99%

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Geographical Entity Annotated Corpus of Japanese Microblogs

Matsuda

Sasaki

Okazaki

et al. 2017

Journal of Information Processing

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This paper addresses the issues in the task of annotating geographical entities on microblogs and reports the preliminary results of our efforts to annotate Japanese microblog texts. Unlike prior work, we aim at annotating not only geographical location entities but also facility entities, such as stations, restaurants and schools. We discuss (i) how to build a gazetteer of geographical entities with a sufficiently broad coverage, (ii) what types ambiguities that need to be considered, (iii) why the annotator tends to disagree, and (iv) what technical problems should be addressed to automate the task of annotating the geographical entities. All the annotation data and the annotation guidelines are publicly available for research purposes from our web site.

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Section: Pilot Categorization: Why Do We Focus Onmentioning

confidence: 99%

“…These studies were evaluated by using a reference corpus such as the TR-CoNLL [9] or LGL (Local-Global Lexicon) [12] corpus. However, these corpora are annotated only by location entities, and not by facility entities.…”

Section: Related Workmentioning

confidence: 99%

Geographical Entity Annotated Corpus of Japanese Microblogs

Matsuda

Sasaki

Okazaki

et al. 2017

Journal of Information Processing

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“…In the past decade, there has been increasing interest on extracting spatial information from web pages such as addresses, phone numbers, zip codes, and then assigning geographic tags to the pages, a process known as geo-tagging [1,13,21]. Documents are given a geographic footprint, i.e., a set of locations; the footprint is often approximated by an MBR.…”

Section: Spatio-textual Searchmentioning

confidence: 99%

Spatio-textual similarity joins

2012

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Given a collection of objects that carry both spatial and textual information, a spatio-textual similarity join retrieves the pairs of objects that are spatially close and textually similar. As an example, consider a social network with spatially and textually tagged persons (i.e., their locations and profiles). A useful task (for friendship recommendation) would be to find pairs of persons that are spatially close and their profiles have a large overlap (i.e., they have common interests). Another application is data de-duplication (e.g., finding photographs which are spatially close to each other and high overlap in their descriptive tags). Despite the importance of this operation, there is very little previous work that studies its efficient evaluation and in fact under a different definition; only the best match for each object is identified. In this paper, we combine ideas from state-of-the-art spatial distance join and set similarity join methods and propose efficient algorithms that take into account both spatial and textual constraints. Besides, we propose a batch processing technique which boosts the performance of our approaches. An experimental evaluation using real and synthetic datasets shows that our optimized techniques are orders of magnitude faster than baseline solutions.

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“…These studies were evaluated by using a reference corpus such as the TR-CoNLL (Leidner, 2007) or LGL(Local-Global Lexicon) (Lieberman et al, 2010) corpus. However, these corpora are annotated only by location entities, and not by facility entities.…”

Section: Related Workmentioning

confidence: 99%

Annotating Geographical Entities on Microblog Text

Matsuda¹,

Sasaki²,

Okazaki³

et al. 2015

Proceedings of the 9th Linguistic Annotation Workshop

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This paper presents a discussion of the problems surrounding the task of annotating geographical entities on microblogs and reports the preliminary results of our efforts to annotate Japanese microblog texts. Unlike prior work, we not only annotate geographical location entities but also facility entities, such as stations, restaurants, shopping stores, hospitals and schools. We discuss ways in which to build a gazetteer, the types of ambiguities that need to be considered, reasons why the annotator tends to disagree, and the problems that need to be solved to automate the task of annotating the geographical entities. All the annotation data and the annotation guidelines are publicly available for research purposes from our web site.

show abstract

Geotagging with local lexicons to build indexes for textually-specified spatial data

Cited by 122 publications

References 16 publications

Geographical Entity Annotated Corpus of Japanese Microblogs

Geographical Entity Annotated Corpus of Japanese Microblogs

Spatio-textual similarity joins

Annotating Geographical Entities on Microblog Text

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