Winstanley, Adam C. and Salaik, B. and Keyes, Laura
(2003)
Statistical language models for topographic data recognition.
In:
2003 IEEE International Geoscience and Remote Sensing Symposium, 2003. IGARSS '03. Proceedings.
IEEE, pp. 1808-1810.
ISBN 0780379292
Abstract
The success of Statistical Language Models (SLMs)
at improving the performance of Natural Language Processing
(NLP) applications suggests their possible applicability to the
area of automated map reading. This idea stems from the fact
that there are similarities between natural language and
cartographic language. We describe a method of using SLM to
characterise the context of different classes of objects. We use
these models to measure the frequency of each feature context.
This can be used to help identify unclassified map features in
combination with other methods (for example, based on an
object’s shape).
| Item Type: |
Book Section
|
| Keywords: |
Statistical language models; Natural language processing; topographic data recognition; automated map reading; |
| Academic Unit: |
Faculty of Science and Engineering > Computer Science |
| Item ID: |
8106 |
| Identification Number: |
https://doi.org/10.1109/IGARSS.2003.1294257 |
| Depositing User: |
Dr. Adam Winstanley
|
| Date Deposited: |
30 Mar 2017 14:17 |
| Publisher: |
IEEE |
| Refereed: |
Yes |
| URI: |
|
| Use Licence: |
This item is available under a Creative Commons Attribution Non Commercial Share Alike Licence (CC BY-NC-SA). Details of this licence are available
here |
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