Papers by Xulang Zhang
Neuro-Symbolic Sentiment Analysis with Dynamic Word Sense Disambiguation (2023.findings-emnlp)
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| Challenge: | Traditional neural network models represent word senses as vectors that are uninterpretable for humans. |
| Approach: | They propose a framework that incorporates word Sense Disambiguation (WSD) by identifying and paraphrasing ambiguous words to improve sentiment predictions. |
| Outcome: | The proposed framework improves sentiment analysis accuracy and interpretability on a downstream task without ground-truth word sense labels. |
Vanessa: Visual Connotation and Aesthetic Attributes Understanding Network for Multimodal Aspect-based Sentiment Analysis (2024.findings-emnlp)
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| Challenge: | Existing methods to analyze images focus on superficial features or descriptions, omitting subtle contextual information. |
| Approach: | They propose a Visual Connotation and Aesthetic Attributes Understanding Network (Vanessa) for Multimodal Aspect-based Sentiment Analysis. |
| Outcome: | The proposed network captures both implicit and explicit sentimental cues and can be used to enrich textual sentiment analysis. |
SenticVec: Toward Robust and Human-Centric Neurosymbolic Sentiment Analysis (2024.findings-acl)
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| Challenge: | Existing approaches to tackle learning challenges such as knowledge forgetting and extensive computing resources are not effective. |
| Approach: | They propose a novel neurosymbolic method for sentiment analysis that places emphasis on human subjectivity within varying domain annotations. |
| Outcome: | The proposed method is lightweight, robust across domains and languages, efficient few-shot training, and rapid convergence. |
GPTEval: A Survey on Assessments of ChatGPT and GPT-4 (2024.lrec-main)
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| Challenge: | emergence of ChatGPT has generated speculation about its potential to disrupt social and economic systems. |
| Approach: | They analyze prior assessments of ChatGPT and GPT-4 to analyze their language and reasoning abilities, scientific knowledge, ethical considerations and existing evaluation methods. |
| Outcome: | The proposed model performs satisfactorily in science knowledge and can answer open questions. |