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Optimization of natural language processing Models for Multilingual Legal Document Analysis 3

Optimization of Natural Language Processing Models for Multi...

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3rd IEEE international conference on Intelligent Techniques in Control, Optimization and Signal processing, INCOS 2024

作者： Radhika, A. Bhasin, Narinder Kumar Sabareesh, R. Raju, Yallapragada Ravi Satyanarayana, K.N.V. Raj, I Infant Srk Institute of Technology Department of Cse Vijayawada India Amity University Zonal Head Indian Institute of Banking and Finance Delhi India Coimbatore India Madanapalle Institute of Technology & Science Department of Computer Science &technology Andhra Pradesh Madanapalle India S.R.K.R. Engineering College Department of Ece A.P India K.Ramakrishnan College of Engineering Department of Computer Science & Engineering Tamil Nadu Trichy India

ISBN: (纸本)9798350361186

Multilingual legal document analysis poses unique challenges in the field of natural language processing (NLP) due to the intricacies of legal language and the diverse linguistic landscape of legal texts across jurisdictions. This paper presents an optimization framework designed to enhance the performance of NLP models specifically tailored for multilingual legal document analysis. The proposed framework incorporates advanced techniques in pre-processing, feature engineering, and model architecture to address the complexities inherent in legal language. Leveraging multilingual embeddings and domain-specific knowledge, the model demonstrates improved accuracy in tasks such as named entity recognition, sentiment analysis, and document categorization across a range of languages. Additionally, the optimization framework emphasizes the importance of domain adaptation, acknowledging the nuances and variations in legal terminology across different legal systems. Through a combination of transfer learning and fine-tuning strategies, the model adapts to specific legal domains, ensuring robust performance in diverse legal contexts. Experimental results on a comprehensive dataset of multilingual legal documents validate the effectiveness of the proposed optimization framework. Comparative analyses with baseline models showcase significant improvements in precision, recall, and overall model performance. The findings underscore the potential of the optimized NLP model for applications in legal information retrieval, contract analysis, and legal knowledge management in a multilingual context. This research contributes to the growing body of knowledge in NLP and legal informatics, offering a valuable resource for researchers, practitioners, and developers working on multilingual legal document analysis. The optimized model presented in this paper has the potential to enhance the efficiency and accuracy of automated systems in handling legal texts across diverse linguistic enviro

关键词： knowledge management

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Emotional knowledge Self-Distillation in Dialogue

Emotional Knowledge Self-Distillation in Dialogue

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

作者： Jian, Zhongquan Wang, Yancheng Wu, Weichao Yao, Junfeng Wang, Meihong Wu, Qingqiang School of Informatics Xiamen University Xiamen China School of Film Xiamen University Xiamen China School of Computing and Data Science Xiamen University Malaysia Selangor Malaysia School of Mechanics and Civil Engineering China University of Mining & Technology Beijing China Xiamen Key Laboratory of Intelligent Storage and Computing School of Informatics Xiamen University Xiamen China Key Laboratory of Digital Protection and Intelligent Processing of Intangible Cultural Heritage of Fujian and Taiwan Ministry of Culture and Tourism Xiamen China

ISBN: (纸本)9798350368741

Recognizing emotions in dialogues is vital for effective human-computer interaction, yet remains a challenging task in natural language processing (NLP). Previous studies in Emotion Recognition in Conversation (ERC) have primarily focused on contextual features, while overlooking the importance of emotional features in emotion recognition. To address this gap, we focus on the role of emotional features in ERC and propose a novel method, Emotional knowledge Self-Distillation (EmoKSD1), to enhance the model's emotional sensitivity. In EmoKSD, utterances are enriched with implicit 〈mask〉 tokens to represent conveyed emotions, allowing the distillation of emotional knowledge from explicit emotional tokens to implicit 〈mask〉 tokens, thereby enhancing the model's ability to perceive subtle emotions within the dialogue. Through thorough evaluations on two public ERC datasets (i.e., IEMOCAP and MELD) using proposed coarse-grained utterance distillation and fine-grained token distillation techniques, EmoKSD demonstrates superior performance compared to existing methods, highlighting the significance of emotional features in ERC. © 2025 IEEE.

关键词： Dialogue System Emotion Recognition in Conversation knowledge Distillation natural language processing

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Evaluation natural language processing knowledge Graphs for Semi-supervised Learning 11th

Evaluation Natural Language Processing Knowledge Graphs for ...

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11th international conference on Robot Intelligence Technology and Applications, RiTA 2023

作者： Mishra, Amaya Kumar Assi, Sulaf Jayabalan, Manoj Assi, Jolnar Al-Jumeily Obe, Dhiya Independent Researcher Mumbai India Liverpool John Moores University Liverpool United Kingdom Traders Island London United Kingdom

ISBN: (纸本)9783031706868

natural language processing knowledge Graphs have gained significant popularity over the last decade in numerous sectors including pharmaceutical and financial sectors. In all sectors, knowledge-based graphs showed to be popular due to their ability in providing relevant information that considers meaning and context. Hence knowledge based graphs have provided better contextual information than other natural language processing systems. Yet knowledge graphs do not always provide complete information about the data analysed. Therefore, this work evaluated the use of knowledge graphs for entities linking and evaluation of large unstructured datasets. Thus involved the use of both semantic and linguistic modelling that when combined with deep learning enabled understanding the dataset and context of it by linking different components of the dataset together. Yet some limitations were encountered when applying these graphs in this study and there were related to computational power. Therefore, future work involves applying this work to larger datasets and with better computational power. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： knowledge graph

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Research and Applications of Large language Models for Converting Unstructured Data into Structured Data 3

Research and Applications of Large Language Models for Conve...

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3rd international conference on Cloud Computing, Big Data Application and Software engineering, CBASE 2024

作者： Liu, Xiangfeng Sun, Jianting Lei, Aidi Zhu, Junzhi Co. Ltd Chengdu610073 China University of Electronic Science and Technology of China School of Information and Software Engineering Chengdu China

ISBN: (纸本)9798331506582

The rapid growth of data has led to a significant increase in unstructured data, such as text, audio, and images, which dominate modern information processing. However, the complexity of unstructured data presents challenges for automated analysis and processing. Converting unstructured data into structured formats is crucial for tasks like data mining, information extraction, and knowledge graph construction. Traditional methods that rely on manual rules or statistical models struggle with complex, context-dependent data. Recently, large language models (LLMs), such as GPT-4 and BERT, have demonstrated great potential in unstructured data processing due to their powerful natural language understanding and generation capabilities. This study explores the application of large language models in transforming unstructured data into structured formats. It begins by reviewing the limitations of traditional approaches and then presents a framework for unstructured data processing using LLMs, covering data preprocessing, information extraction, and structured representation. The study's experiments demonstrate the superior performance of LLMs in handling various types of unstructured data, particularly in tasks like named entity recognition, relation extraction, and contextual understanding. The results show that LLMs achieve higher accuracy and generalization while reducing reliance on manual rules. The paper discusses the strengths and limitations of this method, proposing future improvements, such as combining domain-specific knowledge for model optimization and expanding applications to multimodal data processing. The research highlights the promising role of large language models in converting unstructured data into structured formats. © 2024 IEEE.

关键词： Structured Query language

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ELCoRec: Enhance language Understanding with Co-Propagation of Numerical and Categorical Features for Recommendation 24

ELCoRec: Enhance Language Understanding with Co-Propagation ...

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33rd ACM international conference on Information and knowledge Management (CIKM)

作者： Chen, Jizheng Du, Kounianhua Lin, Jianghao Chen, Bo Tang, Ruiming Zhang, Weinan Yu, Yong Shanghai Jiao Tong Univ Shanghai Peoples R China Huawei Noahs Ark Lab Shanghai Peoples R China

ISBN: (纸本)9798400704369

Large language models have been flourishing in the natural language processing (NLP) domain, and their potential for recommendation has been paid much attention to. Despite the intelligence shown by the recommendation-oriented finetuned models, LLMs struggle to fully understand the user behavior patterns due to their innate weakness in interpreting numerical features and the overhead for long context, where the temporal relations among user behaviors, subtle quantitative signals among different ratings, and various side features of items are not well explored. Existing works only fine-tune a sole LLM on given text data without introducing that important information to it, leaving these problems unsolved. In this paper, we propose ELCoRec to Enhance language understanding with Co-Propagation of numerical and categorical features for Recommendation. Concretely, we propose to inject the preference understanding capability into LLM via a GAT expert model where the user preference is better encoded by parallelly propagating the temporal relations, and rating signals as well as various side information of historical items. The parallel propagation mechanism could stabilize heterogeneous features and offer an informative user preference encoding, which is then injected into the language models via soft prompting at the cost of a single token embedding. To further obtain the user's recent interests, we proposed a novel Recent interaction Augmented Prompt (RAP) template. Experiment results over three datasets against strong baselines validate the effectiveness of ELCoRec.

关键词： Sequential Recommendation Graph Attention Network Prompt engineering Large language Model

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Computer-Assisted knowledge Points and Question Difficulty Classification Based on BERT

Computer-Assisted Knowledge Points and Question Difficulty C...

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2024 international conference on Mechatronics and Intelligent Control, ICMIC 2024

作者： Zhu, Jia Jun Yang, Zichuan Hong, Binjie Song, Jiacheng Wang, Jiwei Chen, Tianhao Yang, Shuilan Lan, Zixun Ma, Fei Department of Information and Computing Science Xi'an Jiaotong-Liverpool University Suzhou China School of Engineering Suzhou Centennial College Suzhou China Department of Applied Mathematics Xi'an Jiaotong-Liverpool University Suzhou China GROW Inc. Suzhou China EasyStack Inc. Nanjing China TAL Education Group Beijing China

ISBN: (数字)9781510686847

ISBN: (纸本)9781510686830

In recent years, with the increasing use of educational technology and online learning platforms, there has been a growing interest in developing intelligent systems that can automatically predict the knowledge points associated with educational questions. This paper presents a novel approach for knowledge point prediction in middle school mathematics questions. The dataset used in this study consists of a large collection of 591,379 middle school mathematics questions. To leverage the power of natural language processing techniques, the questions are preprocessed using a tokenizer and encoded into word embeddings using BERT (Bidirectional Encoder Representations from Transformers). Matrix dot production is then employed to calculate the similarity between each test question and the training set, and the top-n most similar vectors are selected for each test question. A voting mechanism is introduced to eliminate duplicate knowledge points and rank them based on their total scores in descending order, resulting in k candidate solutions. The performance of the proposed approach is evaluated using top-k accuracy as the evaluation metric and a lowest common ancestor (LCA) algorithm is used to calculate accuracy at different levels of the knowledge point tree. The results demonstrate the effectiveness of the proposed approach in predicting knowledge points for middle school mathematics questions and its potential for application in intelligent tutoring systems and educational assessment tools. © 2025 SPIE.

关键词： natural language processing systems

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The Application of LLMs in the Analysis and Modeling of Software Requirements 24

The Application of LLMs in the Analysis and Modeling of Soft...

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24th IEEE international conference on Software Quality, Reliability and Security Companion, QRS-C 2024

作者： Wang, Zhipeng Feng, Changxi Liu, Longfei Jiao, Guotao Ye, Peng School of Computer Science and Artificial Intelligence WuGuotao Jiaohan Textile University China Engineering Research Center of Hubei Province for Clothing Information Wuhan Textile University China

ISBN: (纸本)9798350365658

In modern software engineering, requirements analysis and modeling are key steps in requirements engineering, influencing the subsequent system design and implementation. Traditional requirements analysis and modeling methods usually involve multiple stakeholders such as requirements providers and analysts working together collaboratively and iteratively, requiring a significant amount of manpower. It is of great significance to reduce the burden on requirements providers and analysts and improve the efficiency of analysis and modeling. In existing work, some merely utilize knowledge repositories to provide more knowledge in support of analysis or modeling, while others employ natural language processing (NLP) techniques to automate the analysis or modeling process. However, neither has relieved the burden on requirements providers and analysts. The emergence of large language models(LLMs) has brought new possibilities to requirements analysis and modeling. LLMs can combine knowledge base processing and understanding of large amounts of natural language data, providing a new automated requirement analysis method. This paper proposes an automated requirements analysis and modeling method for Support Vector Machine(SVM) classifier and LLMs - SVM-LLMs. The method is deployed based on the intelligent requirement service platform - 'Wisdom Requirement Communication', which combines SVM and LLMs to effectively improve the accuracy of requirements analysis and significantly reduce the time in the software development process. © 2024 IEEE.

关键词： Application programs

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Improved Evaluator for Subjective Answers Using natural language processing 7th

Improved Evaluator for Subjective Answers Using Natural Lang...

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7th IFIP TC 12 international conference on Computational Intelligence in Data Science, ICCIDS 2024

作者： Sharif, A. Yasin Ravindhar, N.V. Computer Science and Engineering Saveetha Engineering College Chennai India

ISBN: (纸本)9783031699818

Examination is a preliminary process for evaluating our knowledge. While objective examinations consist of multiple-choice questions, which are easy to evaluate using simple methods, whereas subjective examinations, which often require written responses, having a significant challenge in uniform evaluation. This type of answer evaluation is a time-consuming process to undertake. This can lead to inconsistencies and biases in grading due to the stress of examiners having to carry out the same task repeatedly. With the technological advancements in the field of natural language processing, which offers a range of techniques that can be employed to evaluate text responses, we have proposed a novel solution for creating an evaluator that increases the accuracy as near to human evaluators compared to other such existing systems for evaluating subjective answers. This model works by taking inputs like the user response, expected sentences and expected keywords and works with the help of numerical vectors, natural language processing and deep learning approaches and other mathematical calculations to calculate the aggregate score for an answer. © IFIP international Federation for Information processing 2024.

关键词： Deep learning

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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...

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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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Cross-language Entity Alignment Based on Graph Convolution Neural Network and Attribute Information

Cross-language Entity Alignment Based on Graph Convolution N...

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2024 international conference on Computer Network Security and Software engineering, CNSSE 2024

作者： Hu, Xiaozhan Sun, Yuan Minzu University of China Beijing100081 China

ISBN: (纸本)9781510680234

knowledge graphs are widely used in the field of natural language processing applications. In order to study how to use the structural and attribute information of entities for cross language entity alignment, we have successively borrowed the high-speed gate mechanism of the HGCN model and the relationship aware neighborhood matching model of the RNM model. Firstly, using Graph Convolutional Neural Network (GCN) for knowledge graph embedding learning, and then introducing the method of attribute information and highway gates mechanism to jointly embed the structure and attributes for learning. In entity alignment, relationship aware neighborhood matching is used to improve alignment performance. Therefore, this article proposes a research method for entity alignment based on graph convolutional neural networks and attribute information. Experiments were conducted on the publicly available dataset DBP15k, and from the results, it can be seen that Hits@1 The indicators reached 85.24%, 87.26%, and 94.76% respectively, achieving better experimental results. © 2024 SPIE.

关键词： knowledge graph

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