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    <abstract>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.</abstract>
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%0 Conference Proceedings
%T Classification of Semantic Paraphasias: Optimization of a Word Embedding Model
%A McKinney-Bock, Katy
%A Bedrick, Steven
%S Proceedings of the 3rd Workshop on Evaluating Vector Space Representations for NLP
%D 2019
%8 jun
%I Association for Computational Linguistics
%C Minneapolis, USA
%F mckinney-bock-bedrick-2019-classification
%X 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.
%R 10.18653/v1/W19-2007
%U https://aclanthology.org/W19-2007
%U https://doi.org/10.18653/v1/W19-2007
%P 52-62

Markdown (Informal)

[Classification of Semantic Paraphasias: Optimization of a Word Embedding Model](https://aclanthology.org/W19-2007) (McKinney-Bock & Bedrick, 2019)

• Classification of Semantic Paraphasias: Optimization of a Word Embedding Model (McKinney-Bock & Bedrick, 2019)

ACL

• Katy McKinney-Bock and Steven Bedrick. 2019. Classification of Semantic Paraphasias: Optimization of a Word Embedding Model. In Proceedings of the 3rd Workshop on Evaluating Vector Space Representations for NLP, pages 52–62, Minneapolis, USA. Association for Computational Linguistics.