Multi-agent reinforcement learning: weighting and partitioning
Neural Networks
Computer
Scaling to very very large corpora for natural language disambiguation
ACL '01 Proceedings of the 39th Annual Meeting on Association for Computational Linguistics
Feature-rich part-of-speech tagging with a cyclic dependency network
NAACL '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology - Volume 1
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Proactive screening for depression through metaphorical and automatic text analysis
Artificial Intelligence in Medicine
| Hi-index | 0.00 |
We present data demonstrating how brain health may be assessed by applying data-mining and text analytics to patient language. Three brain-based disorders are investigated - Alzheimer's Disease, cognitive impairment and clinical depression. Prior studies identify particular language characteristics associated with these disorders. Our data show computer-based pattern recognition can distinguish language samples from individuals with and without these conditions. Binary classification accuracies range from 73% to 97% depending on details of the classification task. Text classification accuracy is known to improve substantially as training data approaches web-scale. Such a web scale dataset seems inevitable given the ubiquity of social computing and its language intensive nature. Given this context, we claim that the classification accuracy levels obtained in our experiments are significant findings for the fields of web intelligence and applied brain informatics.