| Abstract
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We target in this paper the challenge of e … We target in this paper the challenge of extracting geospatial data from the article text of the English Wikipedia. We present the results of a Hidden Markov Model (HMM) based approach to identify location-related named entities in the our corpus of Wikipedia articles, which are primarily about battles and wars due to their high geospatial content. The HMM NER process drives a geocoding and resolution process, whose goal is to determine the correct coordinates for each place name (often referred to as grounding). We compare our results to a previously developed data structure and algorithm for disambiguating place names that can have multiple coordinates. We demonstrate an overall f-measure of 79.63% identifying and geocoding place names. Finally, we compare the results of the HMM-driven process to earlier work using a Support Vector Machine.rlier work using a Support Vector Machine.
|
| Abstractsub
|
We target in this paper the challenge of e … We target in this paper the challenge of extracting geospatial data from the article text of the English Wikipedia. We present the results of a Hidden Markov Model (HMM) based approach to identify location-related named entities in the our corpus of Wikipedia articles, which are primarily about battles and wars due to their high geospatial content. The HMM NER process drives a geocoding and resolution process, whose goal is to determine the correct coordinates for each place name (often referred to as grounding). We compare our results to a previously developed data structure and algorithm for disambiguating place names that can have multiple coordinates. We demonstrate an overall f-measure of 79.63% identifying and geocoding place names. Finally, we compare the results of the HMM-driven process to earlier work using a Support Vector Machine.rlier work using a Support Vector Machine.
|
| Bibtextype
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inproceedings +
|
| Doi
|
10.1109/ICSC.2010.74 +
|
| Has author
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Daryl Woodward +
, Jeremy Witmer +
, Jugal Kalita +
|
| Has extra keyword
|
Entity extractions +
, F-measure +
, Geo coding +
, Geo-spatial +
, Geo-spatial data +
, Named entities +
, Process drives +
, Resolution process +
, Wikipedia +
, Data structures +
, Hidden Markov models +
, Semantics +
|
| Isbn
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9780769541549 +
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| Language
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English +
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| Number of citations by publication
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0 +
|
| Number of references by publication
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0 +
|
| Pages
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402–407 +
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| Published in
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Proceedings - 2010 IEEE 4th International Conference on Semantic Computing, ICSC 2010 +
|
| Title
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A comparison of approaches for geospatial entity extraction from Wikipedia +
|
| Type
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conference paper +
|
| Year
|
2010 +
|
| Creation dateThis property is a special property in this wiki.
|
6 November 2014 13:27:55 +
|
| Categories |
Publications without keywords parameter +
, Publications without license parameter +
, Publications without remote mirror parameter +
, Publications without archive mirror parameter +
, Publications without paywall mirror parameter +
, Conference papers +
, Publications without references parameter +
, Publications +
|
| Modification dateThis property is a special property in this wiki.
|
6 November 2014 13:27:55 +
|
| redirect page |
A comparison of approaches for geospatial entity extraction from Wikipedia +
|
|
|
|
| DateThis property is a special property in this wiki.
|
2010 +
|
