Refining Semantic Similarity of Paraphasias Using a Contextual Language Model.

ABSTRACT:

Purpose

ParAlg (Paraphasia Algorithms) is a software that automatically categorizes a person with aphasia's naming error (paraphasia) in relation to its intended target on a picture-naming test. These classifications (based on lexicality as well as semantic, phonological, and morphological similarity to the target) are important for characterizing an individual's word-finding deficits or anomia. In this study, we applied a modern language model called BERT (Bidirectional Encoder Representations from Transformers) as a semantic classifier and evaluated its performance against ParAlg's original word2vec model.

Method

We used a set of 11,999 paraphasias produced during the Philadelphia Naming Test. We trained ParAlg with word2vec or BERT and compared their performance to humans

SUBMITTER: Salem AC 

PROVIDER: S-EPMC10023190 | biostudies-literature | 2023 Jan

REPOSITORIES: biostudies-literature