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AI-Powered Legal Documentation Assistant

AI-Powered Legal Documentation Assistant

引用

Pervasive Computing and Social Networking (ICPCSN), international conference on

作者： Kuldeep Vayadande Aditi Bhat Pranav Bachhav Aditya Bhoyar Zulfikar Charoliya Aayush Chavan Department of Information Technology Vishwakarma Institute of Technology Pune India

ISBN: (数字)9798350386349

ISBN: (纸本)9798350386356

Legal systems worldwide vary in structure and principles, reflecting the diverse legal traditions of different countries. the legal system, inherently complex and reliant on meticulous documentation, often faces challenges related to time-consuming manual processes and the potential for human errors. the system proposed provides a transformative solution to the above problems. the system emerges as a groundbreaking solution within the intricate landscape of legal systems which responds to these challenges by seamlessly integrating advanced AI techniques. At its core, OpenAI embeddings takes center stage, demonstrating unparalleled proficiency in document generation, comprehension, and abnormality detection, addressing the complexities ingrained in legal documentation. In contrast to traditional approaches, this system maximizes the versatility of ChatGPT 3.5, allowing it to not only issue commands but also proficiently generate a diverse array of legal documents. By incorporating an understanding module equipped with PyPDF, Amazon Textract, and langchain utilities, the system adeptly handles document intricacies. the utilization of OpenAI Embeddings further enhances natural language understanding. Leveraging sentiment analysis and Named Entity Recognition (NER) in its natural language processing (NLP) toolkit, the system employs an intuitive web interface for irregularities detection. the exploration of AI for automated irregularity detection showcases its transformative potential in ensuring document accuracy within the legal domain. this project, therefore, stands as a beacon of innovation, promising to reshape the dynamics of legal document processing by merging advanced AI capabilities with the unique demands of legal systems.

关键词： Pervasive computing Technological innovation Sentiment analysis Law Social networking (online) Documentation Named entity recognition

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Fine keyword clustering using a thesaurus and example sentences for speech translation

Fine keyword clustering using a thesaurus and example senten...

引用

6～(th) international conference on Spoken language processing

作者： Yumi WAKITA Kenji MATSUI Foshinori SAGISAKA Advanced Technology Research Laboratories Matsushita Electric Industrial Co. Ltd. Kyoto Japan Graduate School of Science and Technology Kobe University Kobe Japan Advanced Technology Research Laboratories Matsushita Electric Industrial Co. Ltd. Kyoto Japan ATR Spoken Language Translation Research Laboratories Kyoto Japan Graduate School of Science and Technology Kobe University Kobe Japan

For robust speech translation, we propose a new language translation method in which speech recognition results are mapped to example sentences using keywords. In this method, the keyword clustering is used to cope with recognition crrors and the wide variety of words that do not appear in the training corpus. Initial classes defined using only thesaurus are redefined by using the dependency between the keywords in limited number of example sentences. the effectiveness of our keyword clustering method is confirmed through example sentence search experiments. these experiments were done using keyword sets of (a) different sentences including keywords not in the example sentences and (b) recognition results those sentences in which recognition errors were obtained. Compared with the search method which uses keyword sets defined by using only a thesaurus, our proposed method offered improved search error rates.

关键词： domain LIS Fine keyword clustering using a thesaurus and example sentences for speech translation

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EVALUATING DIFFERENT information RETRIEVAL ALGORIthMS ON REAL-WORLD DATA

EVALUATING DIFFERENT INFORMATION RETRIEVAL ALGORITHMS ON REA...

引用

6～(th) international conference on Spoken language processing

作者： Manfredweber thomasKemp Interactive Systems Labs Am Fasanengarten 576129 Karlsruhe Germany SONY International (Europe GmbH Stuttgart Advanced Technology Center Hedelfinger Str. 63 70327 Stuttgart Germany

More and more data is produced in the form of videos, which are opaque to textual queries. To allow searching in video data collections, two problems have to be solved: the automatic generation of a searchable index, and the effective search in the automatically produced and therefore imperfect index. the ISL View4You system is a prototype of a video indexing and retrieval system which both generates the index and provides a search engine to access it. An end to end evaluation was carried out using real-world data and queries from naive subjects. From the results it can be concluded, errors of the overall system are not due to the index generation, but are introduced by the information retrieval engine (the search). therefore, the focus of this paper is a comparison of two different search algorithms, LSI (latent semantic indexing) and Okapi (a flavor of the traditional classic vector model approach). the evaluation is carried out on the automatically produced index on a relatively small database, which allows for full manual relevance judgement.

关键词： EVALUATING DIFFERENT information RETRIEVAL ALGORIthMS ON REAL-WORLD DATA ISL

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Large-language-Models (LLM)-Based AI Chatbots: Architecture, In-Depth Analysis and their Performance Evaluation 6th

Large-Language-Models (LLM)-Based AI Chatbots: Architecture,...

引用

6th international conference on Recent Trends in Image processing and Pattern Recognition, RTIP2R 2023

作者： Kumar, Vimal Srivastava, Priyam Dwivedi, Ashay Budhiraja, Ishan Ghosh, Debjani Goyal, Vikas Arora, Ruchika Bennett University Uttar Pradesh Greater Noida India Directorate of Secondary Education Haryana Panchkula India SR University Ananthasagar Telangana Warangal India

ISBN: (纸本)9783031530845

this scholarly article conducts a comparative evaluation of prominent large-scale language models, specifically encompassing Google’s BARD, ChatGPT 3.5, and ChatGPT 4. It offers a comprehensive dissection of each model, elucidating aspects such as architectural structure, utilized training data, and proficiency in natural language processing. the paper also undertakes a systematic assessment of these models in executing diverse tasks like text generation, text completion, and question-answering, paralleling their precision, processing speed, and efficiency. the concluding part of the article explores the prospective application domains of these language models, including but not limited to customer service, education, and healthcare sectors, while proffering suggestions for future research trajectories and technological enhancements. In summary, the study furnishes critical insights into the current status of large-scale language models, shedding light on their potential to augment human-computer interaction and drive forward advancements in the realm of natural language processing technology. © 2024, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Natural language processing systems

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An arXiv Paper Question-Answering System Based on Qwen and RAG

An arXiv Paper Question-Answering System Based on Qwen and R...

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Frontiers technology of information and Computer (ICFTIC), IEEE international conference on

作者： Jiakai Gu Donghong Qin School of Artificial Intelligence Guangxi Minzu University Nanning Guangxi China

ISBN: (数字)9798331541750

ISBN: (纸本)9798331541767

this paper explores combining large language models and retrieval-augmented generation (RAG) techniques to build a question-answering system for academic papers. the system is built upon Qwen2.5 models and open-source tools like LlamaIndex, utilizing the arXiv dataset to implement an end-to-end pipeline from user queries to relevant answers through models including query routing, hybrid retrieval, and answer generation. In the retrieval phase, the system leverages both the BGE-M3 embedding model and the BGE-Reranker-V2-M3 reranking model. the generation stage integrates knowledge from multiple sources using a fine-tuned Qwen2.5 model. Experiments show significant improvements in retrieval accuracy and answer coherence by incorporating Reranker and RAG-Fusion. this study highlights the potential of LLM and RAG technologies in academic applications and offers insights for enhancing paper question-answering systems.

关键词： Visualization Technological innovation Query processing Large language models Scalability Retrieval augmented generation Pipelines Hybrid power systems Multilingual Prompt engineering

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Improving ModSecurity WAF Using a Structured-language Classifier 8th

Improving ModSecurity WAF Using a Structured-Language Classi...

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8th international conference on Future Data and Security Engineering, FDSE 2021

作者： Nguyen, Tri-Chan-Hung Le-Nguyen, Minh-Khoi Le, Dinh-thuan Nguyen, Van-Hoa Tôn, Long-Phuoc Nguyen-An, Khuong VNU-HCM Ho Chi Minh City Viet Nam Verichains Lab Ho Chi Minh City Viet Nam Industrial University Ho Chi Minh City Viet Nam

ISBN: (纸本)9789811680618

Cyber security have always been a major concern for internet applications and the demand for website protection is on the rise. web Application Firewalls (WAFs) services are commonly used, as they are convenient as plug-and-play services and provide protection against multiple type of attacks. However, they have high false positive rates. their rules are often concrete to provide one-size-fit-all services, so rule-based WAFs are either too strict that they block all the incoming requests, or too general that they do not block any malicious requests at all. A feasible solution to concrete rule-based WAFs is applying machine learning approaches, which can help WAFs to monitor and adapt with each specific situation of website and application. Many WAF providers have already migrated to this approaches: Cloudfare, Amazon, Fortinet, etc. Most of these effort concentrated on observing users’ behavior. It is hard to draw a line between normal behavior and malicious behavior, and they require enormous machine learning models. therefore, we have developed a simple machine learning system to categorize the requests and support traditional WAFs, with the goal of eliminating the high false positive rates and providing better a surveillance system for web applications. Our model is based on our observation that legitimate requests to a website are usually similar. the module uses CNN to categorize the network requests and determine whether each incoming request is abnormal. the output of our model is combined with the result of a rule-based WAF (ModSecurity in our implementation) to conclude that should the incoming request be blocked or not. In our experiments, the model greatly improve the ModSecurity WAF with false positive rate reduced from 24% to only 3%, keeping pace with other notable studies on using machine learning models to improve WAFs and only requiring processing time 0.05 s per request. © 2021, Springer Nature Singapore Pte Ltd.

关键词： websites

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the Design and Implementation of an Intelligent Q&A System for Electric Power Safety Regulations Based on Large language Model technology

The Design and Implementation of an Intelligent Q&A System f...

引用

Energy Systems and Electrical Power (ICESEP), international conference on

作者： Bochu Li Yan Jiang Jie Xu Zheng Liu Zhiqiang Sheng Xueying Song Fuqiang Chen Zibing Meng Beijing Fibrlink Communications Co. Ltd Beijing China State Grid Information & TeleCommunication Group Co. Ltd Beijing China State Grid Wuxi Power Supply Company Wuxi Jiangsu China

ISBN: (数字)9798350353563

ISBN: (纸本)9798350353570

In response to the challenges of the vast and scattered content, as well as the low efficiency in learning and searching within the safety regulations in the field of electric power, an intelligent question-answering system has been designed based on large model technology. this system aims to organize knowledge related to safety regulations and improve search efficiency. the system employs natural language processing techniques to categorize entries in safety regulations. It utilizes a vector database for entry vectorization and storage. the integration of large models with safety regulation entries is achieved through a large model development framework, forming a question-answering system. A file management module is constructed using a frontend framework to provide interactive functionality to the question-answering system. the performance experiment of the system on the question answering system shows that the overall accuracy of the system's answer to the safety rules and regulations in the field of electric power is more than 60%, which is professional than the general large model.

关键词： Accuracy Large language models Text summarization Regulation Vectors Question answering (information retrieval) Power industry

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Research on fine-grained image description generation method based on deep learning

Research on fine-grained image description generation method...

引用

IEEE international conference on Civil Aviation Safety and information technology (ICCASIT)

作者： Yechao Wang Wuqiang Shen Chaosheng Yao Xiaosong Su Guangdong Power Grid Corporation Guangzhou China Joint Laboratory on Cyberspace Security China Southern Power Grid Guangzhou China Shenzhen E-Link Information Technology Co. Ltd. Shenzhen China

ISBN: (数字)9798350389418

ISBN: (纸本)9798350389425

With the rapid development of deep learning technology, image description generation has emerged as a hot research direction in the field of artificial intelligence, aiming to interpret image content through automatically generated natural language descriptions. Particularly, fine-grained image description generation, which delves into the detailed information within images to provide richer and more precise descriptions, holds broad application prospects, including aiding navigation for the visually impaired, automatic content creation, and augmented reality, among others. this study explores the technology of fine-grained image description generation, proposing a deep learning model that integrates Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) to enhance the quality and accuracy of image descriptions. We employed a composite loss function to refine the model’s training process and introduced an attention mechanism to better capture key information in images. through experimental validation on multiple public datasets, our model demonstrated superior performance to existing technologies. Additionally, we analyzed key factors affecting model performance and discussed potential directions for future improvements. the outcomes of this study not only provide an effective method for fine-grained image description generation but also pave new pathways for the application of deep learning technology in image understanding and natural language processing.

关键词： Deep learning Training Attention mechanisms Recurrent neural networks Accuracy Navigation Computational modeling Safety Convolutional neural networks Standards

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Analysis of Emotion Recognition System Based on Hybrid Neural Network

Analysis of Emotion Recognition System Based on Hybrid Neura...

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IEEE international conference on Artificial Intelligence and Computer Applications (ICAICA)

作者： Yi Lu Shunfu Wang Yiran Zhang Hanxu Li School of Information Science & Technology Xiamen University Tan Kah Kee College Zhangzhou China School of Finance Jilin University of Finance and Economics Jilin China School of Accounting and Finance Xiamen University Tan Kah Kee College Zhangzhou China School of Civil Engineering Xiamen University Tan Kah Kee College Zhangzhou China

ISBN: (数字)9798350377255

ISBN: (纸本)9798350377262

In order to enhance the resilience and generalisation capacity of the system in the context of minor alterations or noise contamination in the input natural language data, a personalised natural language emotion recognition system based on a hybrid network has been developed. A natural language acquisition module, based on a field-programmable gate array, has been designed for the purpose of collecting personalised natural language information. the preprocessing module facilitates the processing of the collected personalised natural language information, resulting in the generation of a log-Mel spectrum. the emotion recognition module employs a hybrid temporal network comprising a dynamic convolutional temporal network and a short-short-memory network. the speech information features are extracted from the Logmel spectrum through a dynamic convolutional neural network, while the text information features are extracted from the natural language text through a short-short-memory neural network. the speech and text information features are then fused through a fully connected neural network to output personalised emotion recognition results. the system has been demonstrated to be effective in the collection of personalised natural language information, the extraction of speech information features and the completion of personalised natural language emotion recognition. Furthermore, the system has been shown to be capable of maintaining high levels of accuracy in emotion recognition even when noise is present.

关键词： Emotion recognition Accuracy Social networking (online) Natural languages Neural networks Noise Speech recognition Feature extraction Data mining Resilience

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A Parallel Cross-language Retrieval System for Patent Documents

A Parallel Cross-Language Retrieval System for Patent Docume...

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2015 6th IEEE international conference on Software Engineering and Service Science(ICSESS 2015)

作者： Xin Shen Heyan Huang Lingzhi Li Yonggang Huang Beijing Engineering Research Center of High Volume Language Information Processing & Cloud Computing Application Beijing Institute of Technology Overhaul Branch Beijing Electric Power Company

In order to help people obtain useful information from patent documents in different languages. this paper proposes a cross-language retrieval system to search Chinese and English patent documents simultaneously. this system consists of query translation module, document retrieval module and user interaction module. Query translation module is used to translate query based on bilingual dictionaries. Document retrieval module consists of monolingual retrieval system using standard vector space model. In order to retrieve in highly parallel, we use the Map Reduce model to calculate the similarity. User interaction module provides users with interactive mechanism used to improve the retrieval accuracy in the system. It contains two parts: the second translation and relevance feedback. the experimental results show that our system has good performance.

关键词： Patent document cross-language information retrieval parallel retrieval Hadoop

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