An interpretable measure of semantic similarity for predicting eye movements in reading.

ABSTRACT: Predictions about upcoming content play an important role during language comprehension and processing. Semantic similarity as a metric has been used to predict how words are processed in context in language comprehension and processing tasks. This study proposes a novel, dynamic approach for computing contextual semantic similarity, evaluates the extent to which the semantic similarity measures computed using this approach can predict fixation durations in reading tasks recorded in a corpus of eye-tracking data, and compares the performance of these measures to that of semantic similarity measures computed using the cosine and Euclidean methods. Our results reveal that the semantic similarity measures generated by our approach are significantly predictive of fixation durations on reading

SUBMITTER: Kun S 

PROVIDER: S-EPMC10482772 | biostudies-literature | 2023 Aug

REPOSITORIES: biostudies-literature