Paper Info
Title
Detecting cognitive impairments by agreeing on interpretations of linguistic features.
Abstract
Linguistic features have shown promising applications for detecting various cognitive impairments. To improve detection accuracies, increasing the amount of data or the number of linguistic features have been two applicable approaches. However, acquiring additional clinical data could be expensive, and hand-carving features are burdensome. In this paper, we take a third approach, putting forward Consensus Networks (CN), a framework to classify after reaching agreements between modalities. We divide the linguistic features into non-overlapping subsets according to their modalities, let neural networks learn low-dimensional representations that agree with each other. These representations are passed into a classifier network. All neural networks are optimized iteratively. In this paper, we also present two methods that empirically improve the performance of CN. We then present ablation studies to illustrate the effectiveness of modality division. To understand further what happens in Consensus Networks, we visualize the interpretation vectors during training procedures. They demonstrate symmetry in an aggregate manner. Overall, using all of the 413 linguistic features, our models significantly outperform traditional classifiers, which are used by the state-of-the-art papers.
Year
DOI
Venue
2018
10.18653/v1/n19-1146
north american chapter of the association for computational linguistics
Field
DocType
Volume
Modalities,Computer science,Natural language processing,Artificial intelligence,Classifier (linguistics),Cognition,Artificial neural network,Linguistics,Machine learning
Journal
abs/1808.06570
Citations 
PageRank 
References 
0
0.34
11
Authors
3
Name
Order
Citations
PageRank
Zining Zhu1102.33
Jekaterina Novikova26412.97
Frank Rudzicz323144.82