Anil Ramakrishna(Amazon) and Shrikanth Narayanan(University of Southern California)
Abstract:
Psycholinguistic normatives represent various affective and mental
constructs using numeric scores and are used in a variety of applications in natural language processing. They are commonly used
at the sentence level, the scores of which are estimated by extrapolating word level scores using simple aggregation strategies, which
may not always be optimal. In this work, we present a novel approach to estimate the psycholinguistic norms at sentence level. We
apply a multidimensional annotation fusion model on annotations at
the word level to estimate a parameter which captures relationships
between different norms. We then use this parameter at sentence
level to estimate the norms. We evaluate our approach by predicting valence, arousal and dominance on sentences from an annotated
dataset and show improved performance compared to word aggregation schemes.