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- W3125379904 abstract "Probabilistic topic models are a class of unsupervised machine learning models used for understanding the latent topics in a corpus of documents. A new method for combining geographic feature data with text from geo-referenced documents to create topic models that are grounded in the physical environment is proposed. The Geographic Feature Type Topic Model (GFTTM) models each document in a corpus as a mixture of feature type topics and abstract topics. Feature type topics are conditioned on additional observation data of the relative densities of geographic feature types co-located with the document's location referent, whereas abstract topics are trained independently of that information. The GFTTM is evaluated using geo-referenced Wikipedia articles and feature type data from volunteered geographic information sources. A technique for the measurement of semantic similarity of feature types and places based on the mixtures of topics associated with the types is also presented. The results of the evaluation demonstrate that GFTTM finds two distinct types of topics that can be used to disentangle how places are described in terms of its physical features and more abstract topics such as history and culture." @default.
- W3125379904 created "2021-02-01" @default.
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- W3125379904 date "2015-02-05" @default.
- W3125379904 modified "2023-09-23" @default.
- W3125379904 title "Geographic Feature Type Topic Model (GFTTM): grounding topics in the landscape" @default.
- W3125379904 doi "https://doi.org/10.7287/peerj.preprints.816" @default.
- W3125379904 hasPublicationYear "2015" @default.
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