arXiv subjects

Alexander Gelbukh

Publications and source records attributed to Alexander Gelbukh.

57 records · Page 4Linked to original sources

arXiv2019

Sentiment and Sarcasm Classification with Multitask Learning

Sentiment classification and sarcasm detection are both important natural language processing (NLP) tasks. Sentiment is always coupled with sarcasm where intensive emotion is expressed. Nevertheless, most literature considers them as two separate tasks. We argue that knowledge in sarcasm detection can also be beneficial to sentiment classification and vice versa. We show that these two tasks are correlated, and present a multi-task learning-based framework using a deep neural network that models this correlation to improve the performance of both tasks in a multi-task learning setting. Our method outperforms the state of the art by 3-4% in the benchmark dataset.

cs.CL↗

arXiv2019

Multimodal Sentiment Analysis: Addressing Key Issues and Setting up the Baselines

We compile baselines, along with dataset split, for multimodal sentiment analysis. In this paper, we explore three different deep-learning based architectures for multimodal sentiment classification, each improving upon the previous. Further, we evaluate these architectures with multiple datasets with fixed train/test partition. We also discuss some major issues, frequently ignored in multimodal sentiment analysis research, e.g., role of speaker-exclusive models, importance of different modalities, and generalizability. This framework illustrates the different facets of analysis to be considered while performing multimodal sentiment analysis and, hence, serves as a new benchmark for future research in this emerging field.

cs.CL↗

arXiv2018

A distinct approach to diagnose Dengue Fever with the help of Soft Set Theory

Mathematics has played a substantial role to revolutionize the medical science. Intelligent systems based on mathematical theories have proved to be efficient in diagnosing various diseases. In this paper, we used an expert system based on soft set theory and fuzzy set theory named as a soft expert system to diagnose tropical disease dengue. The objective to use soft expert system is to predict the risk level of a patient having dengue fever by using input variables like age, TLC, SGOT, platelets count and blood pressure. The proposed method explicitly demonstrates the exact percentage of the risk level of dengue fever automatically circumventing for all possible (medical) imprecisions.

cs.AI↗