2025
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Neuro-Conceptual Artificial Intelligence: Integrating OPM with Deep Learning to Enhance Question Answering Quality
Xin Kang
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Veronika Shteyngardt
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Yuhan Wang
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Dov Dori
Proceedings of Bridging Neurons and Symbols for Natural Language Processing and Knowledge Graphs Reasoning @ COLING 2025
Knowledge representation and reasoning are critical challenges in Artificial Intelligence (AI), particularly in integrating neural and symbolic approaches to achieve explainable and transparent AI systems. Traditional knowledge representation methods often fall short of capturing complex processes and state changes. We introduce Neuro-Conceptual Artificial Intelligence (NCAI), a specialization of the neuro-symbolic AI approach that integrates conceptual modeling using Object-Process Methodology (OPM) ISO 19450:2024 with deep learning to enhance question-answering (QA) quality. By converting natural language text into OPM models using in-context learning, NCAI leverages the expressive power of OPM to represent complex OPM elements—processes, objects, and states—beyond what traditional triplet-based knowledge graphs can easily capture. This rich structured knowledge representation improves reasoning transparency and answer accuracy in an OPM-QA system. We further propose transparency evaluation metrics to quantitatively measure how faithfully the predicted reasoning aligns with OPM-based conceptual logic. Our experiments demonstrate that NCAI outperforms traditional methods, highlighting its potential for advancing neuro-symbolic AI by providing rich knowledge representations, measurable transparency, and improved reasoning.
2024
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TMAK-Plus at SIGHAN-2024 dimABSA Task: Multi-Agent Collaboration for Transparent and Rational Sentiment Analysis
Xin Kang
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Zhifei Zhang
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Jiazheng Zhou
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Yunong Wu
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Xuefeng Shi
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Kazuyuki Matsumoto
Proceedings of the 10th SIGHAN Workshop on Chinese Language Processing (SIGHAN-10)
The TMAK-Plus team proposes a Multi-Agent Collaboration (MAC) model for the dimensional Aspect-Based Sentiment Analysis (dimABSA) task at SIGHAN-2024. The MAC model leverages Neuro-Symbolic AI to solve dimABSA transparently and rationally through symbolic message exchanges among generative AI agents. These agents collaborate on aspect detection, opinion detection, aspect classification, and intensity estimation. We created 8 sentiment intensity agents with distinct character traits to mimic diverse sentiment perceptions and average their outputs. The AI agents received clear instructions and 20 training examples to ensure task understanding. Our results suggest that the MAC model is effective in solving the dimABSA task and offers a transparent and rational approach to understanding the solution process.
2015
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Learning Salient Samples and Distributed Representations for Topic-Based Chinese Message Polarity Classification
Xin Kang
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Yunong Wu
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Zhifei Zhang
Proceedings of the Eighth SIGHAN Workshop on Chinese Language Processing
2011
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Exploring Emotional Words for Chinese Document Chief Emotion Analysis
Yunong Wu
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Kenji Kita
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Fuji Ren
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Kazuyuki Matsumoto
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Xin Kang
Proceedings of the 25th Pacific Asia Conference on Language, Information and Computation