Frank Xing


2022

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SenticNet 7: A Commonsense-based Neurosymbolic AI Framework for Explainable Sentiment Analysis
Erik Cambria | Qian Liu | Sergio Decherchi | Frank Xing | Kenneth Kwok
Proceedings of the Thirteenth Language Resources and Evaluation Conference

In recent years, AI research has demonstrated enormous potential for the benefit of humanity and society. While often better than its human counterparts in classification and pattern recognition tasks, however, AI still struggles with complex tasks that require commonsense reasoning such as natural language understanding. In this context, the key limitations of current AI models are: dependency, reproducibility, trustworthiness, interpretability, and explainability. In this work, we propose a commonsense-based neurosymbolic framework that aims to overcome these issues in the context of sentiment analysis. In particular, we employ unsupervised and reproducible subsymbolic techniques such as auto-regressive language models and kernel methods to build trustworthy symbolic representations that convert natural language to a sort of protolanguage and, hence, extract polarity from text in a completely interpretable and explainable manner.

2020

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Financial Sentiment Analysis: An Investigation into Common Mistakes and Silver Bullets
Frank Xing | Lorenzo Malandri | Yue Zhang | Erik Cambria
Proceedings of the 28th International Conference on Computational Linguistics

The recent dominance of machine learning-based natural language processing methods has fostered the culture of overemphasizing model accuracies rather than studying the reasons behind their errors. Interpretability, however, is a critical requirement for many downstream AI and NLP applications, e.g., in finance, healthcare, and autonomous driving. This study, instead of proposing any “new model”, investigates the error patterns of some widely acknowledged sentiment analysis methods in the finance domain. We discover that (1) those methods belonging to the same clusters are prone to similar error patterns, and (2) there are six types of linguistic features that are pervasive in the common errors. These findings provide important clues and practical considerations for improving sentiment analysis models for financial applications.

2019

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Business Taxonomy Construction Using Concept-Level Hierarchical Clustering
Haodong Bai | Frank Xing | Erik Cambria | Win-Bin Huang
Proceedings of the First Workshop on Financial Technology and Natural Language Processing