Name
Abstract | ||
|---|---|---|
This paper presents a machine learning method for resolving place references in text, i.e. linking character strings in documents to locations on the surface of the Earth. This is a fundamental task in the area of Geographic Information Retrieval, supporting access through geography to large document collections. The proposed method is an instance of stacked learning, in which a first learner based on a Hidden Markov Model is used to annotate place references, and then a second learner implementing a regression through a Support Vector Machine is used to rank the possible disabiguations for the references that were initially annotated. The proposed method was evaluated through gold-standard document collections in three different languages, having place references annotated by humans. Results show that the proposed method compares favorably against commercial state-of-the-art systems such as the Metacarta geo-tagger and Yahoo! Placemaker. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1007/978-3-642-12326-9_12 | Lecture Notes in Geoinformation and Cartography |
Keywords | Field | DocType |
machine learning,hidden markov model,gold standard,support vector machine | Structured support vector machine,Conditional random field,Online machine learning,Algorithmic learning theory,Active learning (machine learning),Computer science,Geographic information retrieval,Artificial intelligence,Relevance vector machine,Computational learning theory,Machine learning | Conference |
ISSN | Citations | PageRank |
1863-2246 | 18 | 0.93 |
References | Authors | |
18 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Bruno Martins | 1 | 441 | 34.58 |
| Ivo Anastácio | 2 | 40 | 3.42 |
| Pável Calado | 3 | 809 | 55.33 |