Wed, Oct 02, 2019 @ 12:00 PM - 02:00 PM
PhD Candidate: Anil Ramakrishna
Shri Narayanan (chair)
Location: RTH 320
Time: October 2nd, 12 pm.
Title: Computational Models for Multidimensional Annotations of Affect
Abstract: Affect is an integral aspect of human psychology, it acts as a regulator for all our interactions with external stimuli. Affect includes several related concepts such as sentiment, emotion as well as as higher order constructs such as mood and humor. By its nature, it is highly subjective, with different stimuli leading to different responses in people due to varying personal and cultural artifacts. For example, a specific image or audio clip may evoke different emotions in people depending on their personality. Computational modeling of affective dimensions is an important problem in Artificial Intelligence (AI). It covers a variety of tasks such as sentiment analysis, emotion recognition and opinion mining, which often involve supervised training of models using a large number of labeled data instances. However, training labels are difficult to obtain due to the inherent subjectivity of these constructs. Typical approaches to obtain the training labels include collecting opinions from expert or naive annotators, followed by a suitable aggregation.
In this dissertation, we will present our contributions towards building computational models for noisy annotations of affect, specifically in the aggregation of multidimensional annotations. We propose latent variable models to capture annotator behaviors using additive Gaussian noise and matrix factorization, leading to more accurate estimates of the underlying ground truth. We then apply the joint matrix factorization model to the task of sentence level estimation of psycholinguistic normatives. Finally, we highlight our ongoing efforts in estimating agreement on multidimensional annotations.
Audiences: Everyone Is Invited
Contact: Lizsl De Leon