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PRO-HAN: A HETEROGENEOUS GRAPH ATTENTION NETWORK FOR PROFILE-BASED SPOKEN language UNDERSTANDING 49

PRO-HAN: A HETEROGENEOUS GRAPH ATTENTION NETWORK FOR PROFILE...

引用

49th ieee international conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Teng, Dechuan Lu, Chunlin Xu, Xiao Che, Wanxiang Qin, Libo Harbin Inst Technol Res Ctr Social Comp & Informat Retrieval Harbin Peoples R China Cent South Univ Sch Comp Sci & Engn Changsha Peoples R China

ISBN: (纸本)9798350344868;9798350344851

Recently, Profile-based Spoken language Understanding (SLU) has gained increasing attention, which aims to incorporate various types of supplementary profile information (i.e., knowledge Graph, User Profile, Context Awareness) to eliminate the prevalent ambiguities in user utterances. However, existing approaches can only separately model different profile information, without considering their interrelationships or excluding irrelevant and conflicting information within them. To address the above issues, we introduce a Heterogeneous Graph Attention Network to perform reasoning across multiple PROfile information, called PRO-HAN. Specifically, we design three types of edges, denoted as intra-PRO, inter-PRO, and utterance-PRO, to capture interrelationships among multiple PROs. We establish a new state-of-the-art on the ProSLU dataset, with an improvement of approximately 8% across all three metrics. Further analysis experiments also confirm the effectiveness of our method in modeling multi-source profile information.

关键词： Heterogeneous Graph Neural Networks Spoken language Understanding knowledge Graph User Profile Context Awareness

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AI Assisted Domain Modeling Explainability and Traceability 24

AI Assisted Domain Modeling Explainability and Traceability

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ACM/ieee 27th international conference on Model Driven engineering languages and Systems: Companion Proceedings (MODELS)

作者： Silva, Jonathan Univ Luxembourg Belval Esch Sur Alzett Luxembourg

ISBN: (纸本)9798400706226

Domain Models are abstract representations of selected elements in a domain that is created in a collaborative process between domain and modeler experts. The participants share domain knowledge to conceptualize and reason about the elements that will create the domain models. Through this exchange, a comprehensive and accurate representation of the domain is achieved, ensuring that the model captures the relevant aspects and relationships in the domain. Research in Artificial Intelligence (AI) has explored various methods to assist in the creation of domain models from text using natural language processing (NLP) and Machine Learning (ML). Recent advancements with Large language Models (LLMs) have shown that it is possible to create domain models using prompting techniques;however, the generated domain models contain errors and remain constrained by the performance of the LLM used. Despite the impressive capabilities of LLMs to create domain models, it is evident that it does not address the needs of domain and modelers experts that participate in the creation of domain models. Every AI technique has its advantages and limitations that must be integrated with human feedback in a collaboration process. Therefore, we propose an approach that incorporates human-AI collaboration supported by AI assistants that follows a dialogue approach to understand the users needs and purpose to suggest relevant models. Our proposal combines symbolic and subsymbolic AI techniques with explainability and traceability of the decisions that assist to create domain models that are relevant for the users.

关键词： Domain Modeling Large language Models Uncertainty Explainability Traceability

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A Dual-Perspective Metaphor Detection Framework Using Large language Models

A Dual-Perspective Metaphor Detection Framework Using Large ...

引用

2025 ieee international conference on Acoustics, Speech, and Signal processing, ICASSP 2025

作者： Lin, Yujie Liu, Jingyao Gao, Yan Wang, Ante Su, Jinsong School of Informatics Xiamen University Xiamen China

ISBN: (纸本)9798350368741

Metaphor detection, a critical task in natural language processing, involves identifying whether a particular word in a sentence is used metaphorically. Traditional approaches often rely on supervised learning models that implicitly encode semantic relationships based on metaphor theories. However, these methods often suffer from a lack of transparency in their decision-making processes, which undermines the reliability of their predictions. Recent research indicates that LLMs (large language models) exhibit significant potential in metaphor detection. Nevertheless, their reasoning capabilities are constrained by predefined knowledge graphs. To overcome these limitations, we propose DMD, a novel dual-perspective framework that harnesses both implicit and explicit applications of metaphor theories to guide LLMs in metaphor detection and adopts a self-judgment mechanism to validate the responses from the aforementioned forms of guidance. In comparison to previous methods, our framework offers more transparent reasoning processes and delivers more reliable predictions. Experimental results prove the effectiveness of DMD, demonstrating state-of-the-art performance across widely-used datasets. © 2025 ieee.

关键词： dual-perspective large language models metaphor detection metaphor theories self-judgment

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KG-CQAM: knowledge Graph and Mind Mapping-based Complex Question Answering for Large language Models 5

KG-CQAM: Knowledge Graph and Mind Mapping-based Complex Ques...

引用

5th international conference on Control, Robotics, and Intelligent System, CCRIS 2024

作者： Lai, Xuan Tang, Lianggui Zhu, Xiuling Xiao, Liyong Chen, Zhuo Yang, Jiajun The Artificial Intelligence College of Chongqing Technology and Business University Chongqing China Chongqing Key Laboratory of IntelliSense and Blockchain Technology Chongqing China

ISBN: (数字)9781510685871

ISBN: (纸本)9781510685864

Large language Models (LLMs) have achieved significant advancements in the field of natural language processing. However, they still face challenges related to integrity, timeliness, fidelity, and adaptability, often encountering hallucination issues. To address these challenges, knowledge graphs, as structured data structures storing vast knowledge in a clear manner, can be employed to augment LLMs’ knowledge. However, simple retrieval methods are insufficient for complex multi-hop reasoning tasks. Therefore, this paper proposes a novel approach, utilizing the chain of thought, employing mind maps as key prompts, aimed at dynamically reasoning over knowledge graphs by invoking LLMs while preserving graph structure information. Experimental results demonstrate that KG-CQAM not only significantly enhances LLMs’ reasoning capabilities for multi-hop complex questions but also obviates the need for any model training. © 2024 SPIE.

关键词： knowledge graph

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KnowLog: knowledge Enhanced Pre-trained language Model for Log Understanding 24

KnowLog: Knowledge Enhanced Pre-trained Language Model for L...

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46th ieee/ACM international conference on Software engineering, ICSE 2024

作者： Ma, Lipeng Jiang, Sihang Yang, Weidong Fei, Ben Xu, Bo Liang, Jiaqing Zhou, Mingjie Xiao, Yanghua Shanghai Key Laboratory of Data Science School of Computer Science Fudan University Shanghai China School of Computer Science and Technology Donghua University Shanghai China School of Data Science Fudan University Shanghai China

ISBN: (纸本)9798400702174

Logs as semi-structured text are rich in semantic information, making their comprehensive understanding crucial for automated log analysis. With the recent success of pre-trained language models in natural language processing, many studies have leveraged these models to understand logs. Despite their successes, existing pre-trained language models still suffer from three weaknesses. Firstly, these models fail to understand domain-specific terminology, especially abbreviations. Secondly, these models struggle to adequately capture the complete log context information. Thirdly, these models have difficulty in obtaining universal representations of different styles of the same logs. To address these challenges, we introduce KnowLog, a knowledge-enhanced pre-trained language model for log understanding. Specifically, to solve the previous two challenges, we exploit abbreviations and natural language descriptions of logs from public documentation as local and global knowledge, respectively, and leverage this knowledge by designing novel pre-training tasks for enhancing the model. To solve the last challenge, we design a contrastive learning-based pre-training task to obtain universal representations. We evaluate KnowLog by fine-tuning it on six different log understanding tasks. Extensive experiments demonstrate that KnowLog significantly enhances log understanding and achieves state-of-the-art results compared to existing pre-trained language models without knowledge enhancement. Moreover, we conduct additional experiments in transfer learning and low-resource scenarios, showcasing the substantial advantages of KnowLog. Our source code and detailed experimental data are available at https://***/LeaperOvO/KnowLog. © 2024 ieee Computer Society. All rights reserved.

关键词： Computational linguistics

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Assistance for Visually Impaired individuals using Artificial Intelligence and natural language processing

Assistance for Visually Impaired individuals using Artificia...

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2025 ieee international Students' conference on Electrical, Electronics and Computer Science, SCEECS 2025

作者： Sathvig, Sri K. Bharathi.ln, Priyanka Santhosh.m Ramya, J. St. Joseph's College of Engineering Artificial Intelligence and Data Science Chennai India

ISBN: (纸本)9798331529833

People with Visual Impairments are vulnerable to various challenges in their everyday lives ranging from Access to Information, Travelling to places, Recognizing their surroundings, Isolated Lifestyle and much more. The Proposed solution aims to address these socially inclusive problems through the use of Artificial Intelligence, natural language processing, Machine Learning and Computer Vision. The feature index involves using Object Detection models like Yolo v5 for Obstacle avoidance, Integration of Google Map API for Seamless Navigation and NLP based Optical Character Recognition (OCR) for Text Recognition. An average accuracy of 86 percentage was achieved across all models. This solution aims to provide Better Lifestyle, Increased accessibility and Reduced Dependability for the Community of Visually Impaired Individuals. © 2025 ieee.

关键词： Artificial Intelligence Computer Vision Machine Learning natural language processing

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Constructing a Medical Domain Functional knowledge Graph with Large language Models 6

Constructing a Medical Domain Functional Knowledge Graph wit...

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6th ieee international conference on Power, Intelligent Computing and Systems, ICPICS 2024

作者： Ji, Lintian Du, Shuxin Qiu, Yi Xu, Hongyi Guo, Xiaorui School of Engineering Huzhou University Huzhou Key Laboratory of Intelligent Sensing and Optimal Control for Industrial Systems Huzhou China School of Engineering Huzhou University Huzhou Key Laboratory of Intelligent Sensing and Optimal Control for Industrial Systems Huzhou China

ISBN: (纸本)9798350374315

The integration of medicine and artificial intelligence is creating novel opportunities to gather, analyze, and generate groundbreaking medical insights from the vast expanse of medical literature. Although these advancements hold great promise, difficulties including manual annotation and precise data retrieval, and transparency persist. Advanced natural language processing (NLP) techniques and large language models (LLMs) have shown potential in addressing these issues. This paper introduces the Medical knowledge Graph (MedKG), a comprehensive and multidisciplinary medical knowledge graph. By utilizing advanced NLP algorithms, MedKG extracts millions of entities and converts them into triples from a corpus of superior medical research papers published over the last decade. It categorizes unstructured medical information into nine distinct tags, including disease names, treatments, diagnoses, symptoms, genetics, cellular structures of diseases, patient data, medical procedures, and treatment applications, while seamlessly integrating the digital object identifier (DOI) of each paper. As the latest structured database of medical knowledge, MedKG serves as a powerful tool for accelerating medical research and lays the groundwork for constructing a more extensive medical knowledge graph using full-text medical articles. Moreover, our research establishes a foundation for practical knowledge management systems based on text mining, applicable not only to complex medical systems but also to other professional domains. © 2024 ieee.

关键词： knowledge graph

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A Machine Learning Approach for Predicting Personality Types from Text Patterns Using natural language processing(NLP) 8

A Machine Learning Approach for Predicting Personality Types...

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8th international conference on Electronics, Materials engineering and Nano-Technology, IEMENTech 2025

作者： Ganguly, Kinshuk Saha, Subhajit Mondal, Anupam Dey, Monalisa Institute of Engineering & Management Dept. of Computer Science & Engineering Kolkata India

ISBN: (纸本)9798331520762

This study explores the use of natural language processing (NLP) approach to predict Myers-Briggs Type Indicator (MBTI) personality types from Twitter texts. Initially, we approached the problem as a 16-class classification task, but encountered significant challenges due to the lack of instances representing certain class labels adequately. To address this, we decomposed the MBTI types into four dimensions and trained individual classifiers for each dimension. Leveraging the best performing classifiers, we constructed a stacking ensemble model to predict personality classes corresponding to each dimension. The results demonstrate improved accuracy compared to the initial 16-class classification approach. Additionally, the performance of ensemble model is found to be better than the individual classifiers. Thus, ensemble approach paired with the separate dimension based classification method can be used for effective prediction of personality types for psychological profiling. © 2025 ieee.

关键词： natural language processing systems

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Text Simplification Using T5 Model and BART Model 8

Text Simplification Using T5 Model and BART Model

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8th international conference on Electronics, Materials engineering and Nano-Technology, IEMENTech 2025

作者： Das, Soma Basak, Dipan Bhattacharjee, Abir Institute of Engineering and Management Department of Computer Science and Engineering Kolkata India

ISBN: (纸本)9798331520762

Text simplification is an important part of natural language processing. It focuses on transforming complex text into simpler versions while preserving the meaning. This study examines the performance of two advanced models, T5 (Text-to-Text Transfer Transformer) and BART (Bidirectional and Auto-Regressive Transformers), in simplifying text. The models are trained on a dataset containing complex and simplified text pairs. Their performance is evaluated using BLEU, ROUGE, and SARI scores. The results show that both models perform well, but BART achieves better BLEU and ROUGE scores. © 2025 ieee.

关键词： natural language processing systems

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Generating Multiple Choice Questions from Scientific Literature via Large language Models 15

Generating Multiple Choice Questions from Scientific Literat...

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15th ieee international conference on knowledge Graph, ICKG 2024

作者： Luo, Shunyang Tang, Yuqi Jiang, Mingyuan Feng, Kehua Zhang, Qiang Ding, Keyan Zhejiang University - University of Illinois Urbana-Champaign Institute Zhejiang University China Zhejiang University College of Computer Science and Technology China Zhejiang University ZJU-Hangzhou Global Scientific and Technological Innovation Center China

ISBN: (纸本)9798331508821

The rapid development of Large language Models (LLMs) has significantly advanced the field of natural language processing, including the automated generation of Multiple-Choice Questions (MCQs) from scientific literature. This study introduces a systematic method for creating high-quality MCQs using advanced LLMs. We developed a specialized dataset by extracting data from extensive literature within the domain of materials science, focusing on five critical tasks: common science knowledge Q&A, digital data extraction, detailed understanding, reasoning and interpretation, and safety judgments. Leveraging carefully designed prompts of LLMs, we automated the generation process and conducted validation of MCQs to ensure their relevance and accuracy. The resulting dataset not only demonstrates the potential of LLMs in producing diverse MCQs but also serves as a benchmark for evaluating the problem-solving capabilities of different LLMs in materials science. Our experimental results reveal the strengths and weaknesses of these LLMs, providing valuable insights for future applications in science. © 2024 ieee.

关键词： Data mining

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