C4.5: programs for machine learning
C4.5: programs for machine learning
A maximum entropy approach to named entity recognition
A maximum entropy approach to named entity recognition
Foundations and Trends in Databases
Behaviour Monitoring and Interpretation - BMI: Smart Environments, Volume 3 Ambient Intelligence and Smart Environments
Geospatial route extraction from texts
Proceedings of the 1st ACM SIGSPATIAL International Workshop on Data Mining for Geoinformatics
Automatic extraction of destinations, origins and route parts from human generated route directions
GIScience'10 Proceedings of the 6th international conference on Geographic information science
| Hi-index | 0.01 |
Automatic and accurate extraction of destinations in human-generated route descriptions facilitates visualizing text route descriptions on digital maps. Such information further supports research aiming at understanding human cognition of geospatial information. However, as reproted in previous work, the recognition of destinations is not satisfactory. In this paper, we show our approach and achievements in improving the accuracy of destination name recognition. We identified and evaluated multiple features for classifying a named entity to be either "destination" or "non-destination"; after that, we use a simple yet effective post-processing algorithm to improve classification accuracy. Comprehensive experiments confirm the effectiveness of our approach.