Sentence level estimation of psycholinguistic norms using joint multidimensional annotations

TL;DR

This paper introduces a novel multidimensional annotation fusion model to estimate sentence-level psycholinguistic norms directly from word-level annotations, improving over simple aggregation methods.

Contribution

The work presents a new joint multidimensional annotation fusion approach for more accurate sentence-level psycholinguistic norm estimation.

Findings

Outperforms standard word aggregation schemes in predicting sentence norms.

Effectively captures relationships between different normative dimensions.

Provides a scalable method for sentence-level norm estimation in NLP applications.

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 sentence level scores for various normative dimensions and compare with standard word aggregation schemes.