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Artificial Intelligence and natural language processing for Quality Control and Management

Artificial Intelligence and Natural Language Processing for ...

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IEEE international conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA)

作者： Xie, Haiyan Sally Gandla, Sai Ram Bhattacharya, Mangolika Solanki, Pranshoo Zheng, Dingnan Illinois State Univ Dept Technol Normal IL 61761 USA Illinois State Univ Normal IL 61761 USA Univ Illinois Champaign IL USA

ISBN: (纸本)9798350322996;9798350323009

Established engineering standards are facing challenges in adapting to the evolution of novel material properties, designing entirely new materials, and uncovering new mechanisms that transcend intuitive understanding. This research aims to investigate a new framework of domain-specific language models to automatically generate feasible engineering designs based on requirements. The work is anchored in the understanding of natural language processing and the fidelity requirements of machine-learning models for the civil and construction engineering domain. Relevant reports from the Transportation Research Information Service and standard specifications from various departments of transportation were included in the data collection. Another dataset was the summarized literature of 36 highway agencies. After the comparison of Support Vector Machines (SVM), Long Short-Term Memory network, and linear regression, the SVM algorithm was implemented in the framework to support decision-making through textual and tabular communication, and the results showed improvements in accuracy (130%) and F1 Score (65%). In this research, utilizing case-based content extraction alongside reliable statistical uncertainty estimation has shown the potential to generate valuable decision-support tools and recommendation systems for both engineers and managers. The applications encompassing textual content extraction alongside reliable statistical uncertainty estimation demonstrate the potential to create valuable decision-support tools and recommendation systems for engineers and managers.

关键词： Domain-Specific natural language processing Reliability Concrete Construction knowledge Management

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An End-to-End Generative System for Smart Travel Assistant 16

An End-to-End Generative System for Smart Travel Assistant

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16th international conference on knowledge Discovery and Information Retrieval, KDIR 2024 as part of 16th international Joint conference on knowledge Discovery, knowledge engineering and knowledge Management, IC3K 2024

作者： Tuğcu, Miraç Erdinç, Begüm Çıtamak Çekiç, Tolga Akay, Seher Can Uysal, Derya Deniz, Onur Erdem, Erkut Natural Language Processing Department Yapı Kredi Teknoloji Istanbul Turkey Department of Computer Engineering Hacettepe University Ankara Turkey

ISBN: (纸本)9789897587160

Planning a travel with a customer assistant is a multi-stage process that involves information collecting, and usage of search and reservation services. In this paper, we present an end-to-end system of a voice-enabled virtual assistant specifically designed for travel planning in Turkish. This system involves fine-tuned state-of-the-art models of Speech-to-text (STT) and Text-to-speech (TTS) models for increased success in the tourism domain for Turkish language as well as improvements to chatbot experience that can handle complex, multifaceted conversations that are required for planning a travel thoroughly. We detail the architecture of our voice-based chatbot, focusing on integrating STT and TTS engines with a natural language Understanding (NLU) module tailored for travel domain queries. Furthermore, we present a comparative evaluation of speech modules, considering factors such as parameter size and accuracy. Our findings demonstrate the feasibility of voice-based interfaces for streamlining travel planning and booking processes in Turkish language which lacks high-quality corpora of speech and text pairs. © 2024 by SCITEPRESS – Science and Technology Publications, Lda.

关键词： Deep learning

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An Efficient Training Framework for Chinese Text Classification: Integrating Large language Models and Reinforcement Learning 5

An Efficient Training Framework for Chinese Text Classificat...

引用

5th international conference on Artificial Intelligence and Computer engineering

作者： Wang, Teng Xue, Zhenyu China Jiliang Univ Coll Informat Engn Hangzhou Peoples R China

ISBN: (纸本)9798331528911;9798331528928

This study proposes an efficient model training framework aimed at improving the performance of hierarchical classification of Chinese text in low-computation environments. By leveraging the generative capabilities of large language models for data annotation and augmentation, a high-quality dataset is constructed. knowledge distillation techniques are applied to transfer knowledge from a complex teacher model to a lightweight student model, effectively reducing computational resources. Additionally, a reinforcement learning strategy is introduced to further optimize the student model, enabling it to surpass the teacher model in both accuracy and inference efficiency, providing a novel solution for natural language processing and other resource-constrained tasks.

关键词： data labeling knowledge distillation data augmentation reinforcement learning

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NAVMAT: an AI-powered pathway to knowledge sharing on material failures 7

NAVMAT: an AI-powered pathway to knowledge sharing on materi...

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7th international conference of engineering Against Failure (ICEAF)

作者： Melanitis, N. Giannakopoulos, G. Stamatakis, K. Hellen Naval Acad Piraeus Greece NCSR Demokritos Inst Informat & Telecommun Athens Greece

The paper describes the product development methodology, the architecture, the modules and features of the operating prototype, as well as future challenges of NAVMAT, a naval materials failure management system. The fundamental compound of the knowledge management platform is the recording and classification of a failure incident. NAVMAT supports different types of users, from the first Reporter to the Analyst and Forensic Engineer through appropriate workflows. In these workflows the incident-related information can be provided as text, files, images and videos, which can be easily associated to the incident and provide structured information. NAVMAT provides a number of Artificial Intelligence (AI)enabled helpers. During the incident recording, NAVMAT brings into play reactive real-time search which suggests related incidents and literature to facilitate the editor. It also speeds up the classification of incidents by providing AI-suggested labels, chosen from the (multi-lingual) concepts contained in the system Ontology. On the other hand, the intelligent indexing and search infrastructure of the system supports easy identification and retrieval of past incidents, reports and publications, by applying natural language processing. The prototype of the described system has been embedded as a web application validated by potential Users and is being prepared for Operation in a fleet environment.

关键词： natural language processing systems

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Traffic Detection and Forecasting from Social Media Data Using a Deep Learning-Based Model, Linguistic knowledge, Large language Models, and knowledge Graphs 16

Traffic Detection and Forecasting from Social Media Data Usi...

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16th international conference on knowledge engineering and Ontology Development, KEOD 2024 as part of 16th international Joint conference on knowledge Discovery, knowledge engineering and knowledge Management, IC3K 2024

作者： Melhem, Wasen Abdi, Asad Meziane, Farid Department of Computing and Mathematics Faculty of Science and Engineering University of Derby United Kingdom

ISBN: (纸本)9789897587160

Traffic data analysis and forecasting is a multidimensional challenge that extracts details from sources such as social media and vehicle sensor data. This study proposes a three-stage framework using Deep Learning (DL) and natural language processing (NLP) techniques to enhance the end-to-end pipeline for traffic event identification and forecasting. The framework first identifies relevant traffic data from social media using NLP, context, and word-level embeddings. The second phase extracts events and locations to dynamically construct a knowledge graph using deep learning and slot filling. A domain-specific large language model (LLM), enriched with this graph, improves traffic information relevancy. The final phase integrates Allen's interval algebra and region connection calculus to forecast traffic events based on temporal and spatial logic. This framework’s goal is to improve the accuracy and semantic quality of traffic event detection, bridging the gap between academic research and real-world systems, and enabling advancements in intelligent transport systems (ITS). © 2024 by SCITEPRESS – Science and Technology Publications, Lda.

关键词： knowledge graph

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Negation Handling and Domain Generalization in Sentiment Analysis using Machine Learning Models

Negation Handling and Domain Generalization in Sentiment Ana...

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2024 international conference on knowledge engineering and Communication Systems, ICKECS 2024

作者： Biradar, Shrnivas Raju, G.T. Divakar, K.M. SJC Institute of Technology Dept. of Computer Science and Engineering Chikkaballapur India SJC Institute of Technology Dept. of Information Science and Engineering Chikkaballapur India

ISBN: (纸本)9798350359688

Conventional lexicon-based approaches to sentiment analysis typically lack the necessary methods to properly identify the negation window, making it impossible to model negation. An enormous increase in sentiment-rich electronic and social media has been observed daily. Negation modifiers cause problems for Sentiment Classification techniques and have the power to entirely change the discourse's meaning. Therefore, it becomes essential to manage them well. Opinion mining or sentiment analysis is the study of people's attitudes, feelings, and views as they are expressed in written language. It is one of the busiest text mining and natural language processing research projects. Even though sentiment analysis research has gained popularity in the field of natural language processing, for this problem, the state-of-the-art machine learning approach is based on Bag of Words. But the BOW model pays little attention to polarity shift, which could have a distinct overall effect. One of the main issues with doing sentimental analysis on any given text or sentence is handling polarity shift, which is what this study attempts to address. Sentiment analysis use natural language processing principles to identify negation in the text. Our goal is to identify the negation effect on customer reviews that, although appearing good, are actually negative. The suggested modified negation methodology helps to increase classification accuracy by providing a method for computing negation identification. In terms of review classification by accuracy, precision, and recall, this approach yielded a noteworthy outcome. When test and training data are from distinct domains, machine learning faces the challenge of domain generalization. Despite the large body of research on cross-domain text classification, the majority of current methods concentrate on one-to-one or many-to-one domain adaptation. Our domain generalization method regularly outperforms state-of-the-art domain adaption methods, a

关键词： natural language processing systems

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MLLMIE: Multiple Large language Model Information Extraction in Ancient Medical Texts 7

MLLMIE: Multiple Large Language Model Information Extraction...

引用

7th international conference on Machine Learning and natural language processing, MLNLP 2024

作者： Wang, Feilong Shi, Donghui Cui, Xinyi School of Electronics and Information Engineering Anhui Jianzhu University Mass Spectrometry Key Technology R&D and Clinical Application of Anhui Province Jointly Constructed Discipline Key Experiments Hefei China School of Electronics and Information Engineering Anhui Jianzhu University Hefei China

ISBN: (纸本)9798350354973

Ancient medical texts, which reflect traditional cultural practices, can play an important role in the promotion of medical traditions. Nevertheless, the complexity of extracting information from classical Chinese medical texts and the potential inconsistencies and hallucinations in large language models during information extraction present significant challenges. In domains such as medicine, where the precision of information extraction is of paramount importance, the paper augmented existing methodologies for information extraction from large language models and proposed a novel approach, designated as Multiple Large language Model Information Extraction (MLLMIE). The method initially defined a pattern layer to explicitly delineate the concepts, terms, and their interrelationships within the text. Subsequently, it employed prompt engineering to extract information from multiple models, thereby obtaining candidate medical ancient text knowledge results. Finally, the model validation layer was employed to filter the candidate results, thereby obtaining the final medical ancient text knowledge. The method attains F1 scores of 92.68% for named entity recognition and 94.91% for relation extraction in ancient medical texts, thereby demonstrating high-quality medical ancient text information extraction. This method effectively enhances the accuracy of ancient medical text information extraction and facilitates the discovery and application of ancient medical text knowledge. © 2024 IEEE.

关键词： Information retrieval

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The Accuracy of Domain Specific and Descriptive Analysis Generated by Large language Models 48

The Accuracy of Domain Specific and Descriptive Analysis Gen...

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48th Annual IEEE international Computers, Software, and Applications conference (COMPSAC) - Digital Development for a Better Future

作者： Otieno, Denish Omondi Abri, Faranak Siami-Namini, Sima Namin, Akbar Siami Texas Tech Univ Lubbock TX 79409 USA San Jose State Univ San Jose CA 95192 USA Johns Hopkins Univ Baltimore MD USA

ISBN: (纸本)9798350376975;9798350376968

Large language models (LLMs) have attracted considerable attention as they are capable of showcasing impressive capabilities generating comparable high-quality responses to human inputs. LLMs, can not only compose textual scripts such as emails and essays but also executable programming code. Contrary, the automated reasoning capability of these LLMs in performing statistically-driven descriptive analysis, particularly on user-specific data and as personal assistants to users with limited background knowledge in an application domain who would like to carry out basic, as well as advanced statistical and domain-specific analysis is not yet fully explored. More importantly, the performance of these LLMs has not been compared and discussed in detail when domain-specific data analysis tasks are needed. Additionally, the use of LLMs in isolation is often at times insufficient for creating powerful applications and the real potential comes when LLMs are combined with other sources of computation such as LangChain. This study, consequently, explores whether LLMs can be used as generative AI-based personal assistants to users with minimal background knowledge in an application domain infer key data insights. To demonstrate the performance of the LLMs, the study reports a case study through which descriptive statistical analysis, as well as natural language processing (NLP) based investigations, are performed on a number of phishing emails with the objective of comparing the accuracy of the results generated by LLMs to the ones produced by analysts. The experimental results show that LangChain and the Generative Pre-trained Transformer (GPT-4) excel in numerical reasoning tasks i.e., temporal statistical analysis, achieve competitive correlation with human judgments on feature engineering tasks while struggle to some extent on domain specific knowledge reasoning, where domain-specific knowledge is required.

关键词： Large language Models LangChain Generative Pre-trained Transformer natural language processing Phishing Emails

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The Impact of Large language Modeling on natural language processing in Legal Texts: A Comprehensive Survey 15

The Impact of Large Language Modeling on Natural Language Pr...

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15th international conference on knowledge and Systems engineering, KSE 2023

作者： Anh, Dang Hoang Do, Dinh-Truong Tran, Vu Minh, Nguyen Le Japan Advanced Institute of Science and Technology Japan The Institute of Statistical Mathematics Japan

ISBN: (纸本)9798350329742

natural language processing (NLP) has witnessed significant advancements in recent years, particularly with the emergence of large language models. These models, such as GPT-3.5 and its variants, have revolutionized various domains, including legal text processing (LTP). This survey explores the impact of large language modeling on NLP in the context of legal texts. By analyzing the latest research and developments, we seek to understand the benefits, challenges, and potential applications of large language models in the field of legal language processing. © 2023 IEEE.

关键词： Modeling languages

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Designing and Evaluating a Comprehensive Benchmark for Deep Learning Algorithms in Medical natural language processing 15

Designing and Evaluating a Comprehensive Benchmark for Deep ...

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15th international conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Gill, Amandeep Shreenidhi, H.S. Shrivastav, Amit Kumar Vivekananda Global University Department of R&d Jaipur India School of Engineering and Technology Department of Computer Science and Engineering Karnataka Bangalore India Arka Jain University Office of Registrar Jharkhand Jamshedpur India

ISBN: (纸本)9798350370249

This paper gives a way for the comprehensive benchmarking of deep getting to know algorithms for scientific natural language processing (NLP). The authors suggest a benchmarking technique that includes both shallow and deep gaining knowledge of algorithms that cover a number of medical NLP responsibilities. Moreover, they examine the overall performance of algorithms on diverse clinical datasets and examine the overall performance of the algorithms via extraordinary assessment metrics. Furthermore, they present the effects in their proposed benchmarking on a number of datasets and describe how their consequences can assist practitioners in choosing appropriate algorithms to assess clinical NLP packages. Finally, the authors speak their destiny paintings in phrases of improving the benchmarking process. © 2024 IEEE.

关键词： Algorithms Benchmarking Extraordinary language Performance Responsibilities

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