activity
20182020
most citedLearning Relationships between Text, Audio, and Video via Deep Canonical Correlation for Multimodal Language Analysis

26 citations · 42 across the 3 of their papers we have counts for

collaborators

5 papers

cs.CL20206 cited

Medical symptom recognition from patient text: An active learning approach for long-tailed multilabel distributions

Ali Mottaghi, Prathusha K Sarma, Xavier Amatriain +2

We study the problem of medical symptoms recognition from patient text, for the purposes of gathering pertinent information from the patient (known as history-taking). A typical pa…

cs.LG201926 cited

Learning Relationships between Text, Audio, and Video via Deep Canonical Correlation for Multimodal Language Analysis

Zhongkai Sun, Prathusha Sarma, William Sethares +1

Multimodal language analysis often considers relationships between features based on text and those based on acoustical and visual properties. Text features typically outperform no…

cs.IR2019

Shallow Domain Adaptive Embeddings for Sentiment Analysis

Prathusha K Sarma, Yingyu Liang, William A Sethares

This paper proposes a way to improve the performance of existing algorithms for text classification in domains with strong language semantics. We propose a domain adaptation layer…

cs.IR201910 cited

Multi-modal Sentiment Analysis using Deep Canonical Correlation Analysis

Zhongkai Sun, Prathusha K Sarma, William Sethares +1

This paper learns multi-modal embeddings from text, audio, and video views/modes of data in order to improve upon down-stream sentiment classification. The experimental framework a…

cs.CL2018

Domain Adapted Word Embeddings for Improved Sentiment Classification

Prathusha K Sarma, YIngyu Liang, William A Sethares

Generic word embeddings are trained on large-scale generic corpora; Domain Specific (DS) word embeddings are trained only on data from a domain of interest. This paper proposes a m…