Search Results for author:
Found 8 papers, 2 papers with code
How Interpretable are Reasoning Explanations from Prompting Large Language Models?
We present a comprehensive and multifaceted evaluation of interpretability, examining not only faithfulness but also robustness and utility across multiple commonsense reasoning benchmarks.
Plausible Extractive Rationalization through Semi-Supervised Entailment Signal
The increasing use of complex and opaque black box models requires the adoption of interpretable measures, one such option is extractive rationalizing models, which serve as a more interpretable alternative.
A Comprehensive Review on Financial Explainable AI
The success of artificial intelligence (AI), and deep learning models in particular, has led to their widespread adoption across various industries due to their ability to process huge amounts of data and learn complex patterns.
Recent Developments in Recommender Systems: A Survey
The objective of this study is to provide an overview of the current state-of-the-art in the field and highlight the latest trends in the development of recommender systems.
FinXABSA: Explainable Finance through Aspect-Based Sentiment Analysis
This paper presents a novel approach for explainability in financial analysis by deriving financially-explainable statistical relationships through aspect-based sentiment analysis, Pearson correlation, Granger causality & uncertainty coefficient.
Aspect-Based Sentiment Analysis
Aspect-Based Sentiment Analysis (ABSA)
+1
Polarity and Subjectivity Detection with Multitask Learning and BERT Embedding
The proposed approach reports baseline performances for both polarity detection and subjectivity detection.
Can a Humanoid Robot be part of the Organizational Workforce? A User Study Leveraging Sentiment Analysis
In this study, we propose to have a humanoid social robot, Nadine, as a customer service agent in an open social work environment.
PhonSenticNet: A Cognitive Approach to Microtext Normalization for Concept-Level Sentiment Analysis
The usage of microtext poses a considerable performance issue in concept-level sentiment analysis, since models are trained on standard words.
