Sentence level estimation of psycholinguistic norms using joint multidimensional annotations
This work addresses a specific bottleneck in natural language processing for researchers and practitioners using psycholinguistic norms, but it is incremental as it improves upon existing aggregation methods rather than introducing a new paradigm.
The authors tackled the problem of estimating psycholinguistic norms at the sentence level, which is typically done by aggregating word-level scores, and found that their novel approach using a multidimensional annotation fusion model outperformed standard aggregation schemes.
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.