Classification of Semantic Paraphasias: Optimization of a Word Embedding Model
Katy
McKinney-Bock
author
Steven
Bedrick
author
2019-06
text
Proceedings of the 3rd Workshop on Evaluating Vector Space Representations for NLP
Anna
Rogers
editor
Aleksandr
Drozd
editor
Anna
Rumshisky
editor
Yoav
Goldberg
editor
Association for Computational Linguistics
Minneapolis, USA
conference publication
In clinical assessment of people with aphasia, impairment in the ability to recall and produce words for objects (anomia) is assessed using a confrontation naming task, where a target stimulus is viewed and a corresponding label is spoken by the participant. Vector space word embedding models have had inital results in assessing semantic similarity of target-production pairs in order to automate scoring of this task; however, the resulting models are also highly dependent upon training parameters. To select an optimal family of models, we fit a beta regression model to the distribution of performance metrics on a set of 2,880 grid search models and evaluate the resultant first- and second-order effects to explore how parameterization affects model performance. Comparing to SimLex-999, we show that clinical data can be used in an evaluation task with comparable optimal parameter settings as standard NLP evaluation datasets.
mckinney-bock-bedrick-2019-classification
10.18653/v1/W19-2007
https://aclanthology.org/W19-2007
2019-06
52
62