| Daryl Woodward|
(Alternative names for this author)
|Co-authors||Chasin R., Jeremy Witmer, Jugal Kalita|
|Authorship||Publications (2), datasets (0), tools (0)|
|Citations||Total (0), average (0), median (0), max (0), min (0)|
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Only those publications related to wikis are shown here.
|Title||Keyword(s)||Published in||Language||DateThis property is a special property in this wiki.||Abstract||R||C|
|Extracting and displaying temporal and geospatial entities from articles on historical events||Geospatial entity extraction
Natural Language Processing
|Computer Journal||English||2014||This paper discusses a system that extracts and displays temporal and geospatial entities in text. The first task involves identification of all events in a document followed by identification of important events using a classifier. The second task involves identifying named entities associated with the document. In particular, we extract geospatial named entities. We disambiguate the set of geospatial named entities and geocode them to determine the correct coordinates for each place name, often called grounding. We resolve ambiguity based on sentence and article context. Finally, we present a user with the key events and their associated people, places and organizations within a document in terms of a timeline and a map. For purposes of testing, we use Wikipedia articles about historical events, such as those describing wars, battles and invasions. We focus on extracting major events from the articles, although our ideas and tools can be easily used with articles from other sources such as news articles. We use several existing tools such as Evita, Google Maps, publicly available implementations of Support Vector Machines, Hidden Markov Model and Conditional Random Field, and the MIT SIMILE Timeline.||0||0|
|A comparison of approaches for geospatial entity extraction from Wikipedia||Proceedings - 2010 IEEE 4th International Conference on Semantic Computing, ICSC 2010||English||2010||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.||0||0|