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Analysis of natural language processing Techniques and Tools for Requirements Elicitation: A Systematic Literature Review 12

Analysis of Natural Language Processing Techniques and Tools...

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12th international conference in Software engineering Research and Innovation, CONISOFT 2024

作者： Torres-Igartua, Maria Jose Sanchez-Garcia, Angel J. Ocharan-Hemandezss, Jorge Octavio Vazquez-Reyest, Sodel Universidad Veracruzana Facultad de Estadística e Informática Veracruz Xalapa Mexico Unidad Académica de Ingeniería Eléctrica Universidad Autónoma de Zacatecas Zacatecas Mexico

ISBN: (纸本)9798331532116

Requirements engineering (RE) encompasses ac-tivities such as requirements elicitation, analysis, specification, and validation, which are essential in software development for defining and aligning stakeholder needs and expectations. However, these processes are often laborious and prone to errors and misunderstandings, resulting in systems that fail to meet expectations and require costly revisions. This study specifically focuses on requirements elicitation, as accurate and comprehensive gathering of requirements is vital for the success of subsequent activities. The aim is to explore how natural language processing (NLP) techniques and tools can enhance requirements elicitation, emphasizing their adapt-ability to diverse linguistic contexts. A Systematic Literature Review (SLR) identified the most commonly used Artificial Intelligence (AI) techniques in NLP, such as Support Vector Machine (SVM), Random Forest (RF), and Transformer-based approaches like Bidirectional Encoder Representations from Transformers (BERT). The natural language Toolkit (NLTK) is recognized as a prominent tool in the broader NLP domain, while the ELICitation Aid (ELICA) tool stands out for its application in requirement elicitation. Despite the advantages offered by these approaches, it is crucial to acknowledge and address their limitations. Further research is recommended to enhance their effectiveness, particularly in adapting to languages other than English. © 2024 IEEE.

关键词： Software design

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language Detection Using natural language processing 9

Language Detection Using Natural Language Processing

引用

9th international conference on Advanced Computing and Communication Systems, ICACCS 2023

作者： Rajanak, Yutika Patil, Ratna Singh, Yadvendra Pratap Uttar Pradesh Greater Noida India Noida Institute of Engineering and Technology AIML Department Uttar Pradesh Greater Noida India Manipal University Jaipur Computer Science and Engineering Department Rajasthan Jaipur India

ISBN: (纸本)9798350397376

natural language processing (NLP) is a method for correctly identifying text based on the provided content or topic matter. An extensive study will make it simple to interpret any language and comprehend what is being said. Despite the fact that NLP is a challenging technique, notable examples include Siri and Alexa. natural language detection allows us to determine the language being used in a given document. A Python-written model that has been utilised in this work can be used to analyse the basic linguistics of any language. The "words"that make up sentences are the essential building blocks of knowledge and its expression. Correctly identifying them and comprehending the situation in which they are used are essential. NLP steps in to help us in this circumstance by making it easier for us to identify the linguistics used in a particular piece of information, whether it be written or vocal. NLP gives computers the ability to understand human language and respond correctly, performing language detection for us. The current paper provides a summary of developments in tongue process, including analysis, establishment, various areas of rapid advancement in natural language processing research, development tools, and techniques. © 2023 IEEE.

关键词： Machine learning

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AI-Powered Mental Health Assessment using Emotion Detection for Real-Time Analysis

AI-Powered Mental Health Assessment using Emotion Detection ...

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2025 IEEE international conference on Computational, Communication and Information Technology, ICCCIT 2025

作者： Vijay, K. Raghakeerthana, R. Milinda, Renata Rachael Thusheel, S. Rajalakshmi Engineering College Department of Artificial Intelligence and Machine Learning Chennai India

ISBN: (纸本)9798331512965

This paper is an AI-driven mental health assessment tailored to each patient's distinct emotional needs, addressing the global mental health crisis. Using advanced machine learning and natural language processing, it aims to deliver personalized guidance for psychological and physical wellness activities, along with tools for tracking individual progress. The papers employ exploratory case studies and knowledge discovery techniques to evaluate the effectiveness of these interventions. Data mining methods reveal hidden patterns within mental health datasets, with the potential to reshape therapeutic paradigms. Expected outcomes include enhanced accessibility to mental health resources, early intervention, personalized progress monitoring, improved quality of life, and reduced stigma. By integrating sophisticated data processing, machine learning, and empathetic user interaction, it operates by redefining mental health support. The system's architecture is designed to promote both emotional and cognitive well-being, fostering positive change in mental health care delivery. © 2025 IEEE.

关键词： Computer Vision conversational bot Convolution neural network Emotion detection Machine learning natural language processing Real-Time Analysis

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RGR-KBQA: Generating Logical Forms for Question Answering Using knowledge-Graph-Enhanced Large language Model 31

RGR-KBQA: Generating Logical Forms for Question Answering Us...

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31st international conference on Computational Linguistics, COLING 2025

作者： Feng, Tengfei He, Liang School of Computer Science and Technology Xinjiang University Urumqi830017 China Xinjiang Key Laboratory of Signal Detection and Processing Urumqi830017 China Department of Electronic Engineering Beijing National Research Center for Information Science and Technology Tsinghua University Beijing100084 China

ISBN: (纸本)9798891761964

In the field of natural language processing, knowledge Base Question Answering (KBQA) is a challenging task that involves accurately retrieving answers from structured knowledge. Existing methods often face issues when generating query statements using LLMs, as the knowledge introduced may be imprecise and the models themselves may exhibit hallucination problems, leading to low accuracy, particularly when dealing with complex questions. To address these challenges, we introduce a novel semantic parsing approach called RGR-KBQA, which adopts a Retrieve-Generate-Retrieve framework. The first retrieval step introduces factual knowledge from a knowledge graph to enhance the semantic understanding capabilities of LLMs, thereby improving generation accuracy of logical form. The second step uses a fine-tuned model to generate the logical form, and the final step involves unsupervised relation and entity retrieval to further enhance generation accuracy. These two retrieval steps help alleviate the hallucination problems inherent in LLMs. Experimental results show that RGR-KBQA demonstrate promising performance on CWQ and WebQSP datasets. © 2025 Association for Computational Linguistics.

关键词： Question answering

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A Multi-Agent Collaborative Framework for Constructing knowledge Graphs from Text 15

A Multi-Agent Collaborative Framework for Constructing Knowl...

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15th IEEE international conference on knowledge Graph, ICKG 2024

作者： Chen, Gui Liu, Xianhui Tongji University College of Electronic and Information Engineering Shanghai China

ISBN: (纸本)9798331508821

Recent advancements in large language models (LLMs) have significantly improved natural language understanding and generation, making them valuable tools for knowledge graph construction. However, a single LLM often struggles with the complexity of this task, leading to suboptimal results. To address this challenge, we propose a robust multi-agent collaborative framework for constructing knowledge graphs from text. This framework leverages dynamic interactions among specialized agents, including knowledge graph experts, knowledge extraction experts, data processing experts, and domain-specific experts, to effectively build accurate knowledge graphs from text. Additionally, we introduce a novel prompt construction method tailored for knowledge extraction and a revision mechanism to revise preliminary knowledge graphs. These innovations address common issues in knowledge extraction and enhance the quality of model-generated content. Experimental results on four datasets across two tasks (Named Entity Recognition and Relation Extraction) demonstrate that our approach achieves superior performance in the F1 score compared to baseline methods, highlighting its effectiveness and robustness. © 2024 IEEE.

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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AI-Based Assistance for Management of Oral Community knowledge in Low-Resource and Colloquial Kannada language 11th

AI-Based Assistance for Management of Oral Community Knowled...

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11th international conference on Big Data Analytics in Astronomy, Science, and engineering (BDA)

作者： Aparna, M. Srivatsa, Sharath Madhavan, G. Sai Dinesh, T. B. Srinivasa, Srinath Int Inst Informat Technol 26-CElect City Phase 1 Bangalore Karnataka India IruWay Rural Res Lab Janastu Durgadahalli India

ISBN: (纸本)9783031585012;9783031585029

knowledge in rural communities is largely created, preserved, and is transferred verbally, and it is limited. This information is valuable to these communities, and managing and making it available digitally with state-of-the-art approaches enriches awareness and collective knowledge of people of these communities. The large amounts of data and information produced on the Internet are inaccessible to the population in these rural communities due to factors like lack of infrastructure, connectivity, and limited literacy. knowledge internal to rural communities is also not conserved and made available in any global Big Data information systems. Artificial Intelligence (AI) technologies such as Automatic Speech Recognition (ASR) and natural language processing (NLP) provide substantial assistance when vast quantities of data, like Big Data, are available to build solutions. In the case of low-resource languages like Kannada and rural colloquial dialects, publicly available corpora are significantly less. Building state-of-the-art AI solutions is challenging in this context, and we address this problem in this work. knowledge management in rural communities requires a low-cost and efficient approach that social workers can use. This paper proposes an architecture for oral knowledge management for rural communities speaking colloquial Kannada. The proposed architecture has an interface for oral knowledge retrieval using text processing on transcripts generated from the smallest state-of-the-art ASR model. We propose three interfaces to search for content: an n-gram based fuzzy search to search for texts in audios, the most frequent entities search based on the Kannada Named Entity Recognition (NER) model, and question answering with Large language Model (LLM) using a community knowledge vector store.

关键词： Community knowledge Management Low-resource languages Automatic Speech Recognition Keyword Search Named Entity Recognition Large language Model Big Data

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The Application of Fine-Tuning on Pretrained language Model in Information Extraction for Fault knowledge Graphs 9

The Application of Fine-Tuning on Pretrained Language Model ...

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9th international conference on Intelligent Computing and Signal processing, ICSP 2024

作者： Zhang, Kaiwen Su, Feiyu Huang, Yixiang Li, Yanming Wu, Fengqi Mao, Yuhan School of Mechanical and Power Engineering Shanghai Jiao Tong University Shanghai200240 China Shanghai Jiao Tong University The State Key Laboratory of Mechanical System and Vibration Shanghai200240 China Shanghai Institute of Special Equipment Inspection and Technical Research Shanghai200333 China

ISBN: (纸本)9798350376548

Constructing fault knowledge graphs holds significant importance for achieving intelligent maintenance and diagnosis in high-end equipment manufacturing. Effective information extraction and knowledge graph construction have proven challenging due to the lack of standardized representation of semantically complex unstructured text in the industrial domain. Therefore, in this study, we performed fine-tuning on the pre-trained language model (ChatGLM2-6B) with specific prompts to achieve information extraction from fault-related texts, ultimately leading to the construction of a fault knowledge graph. Experimental results demonstrate that the proposed method not only supports fine-tuning with limited data but also exhibits enhanced capability in understanding complex semantics related to fault symptoms and causes. © 2024 IEEE.

关键词： knowledge graph

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Descriptive Answer Evaluation System using natural language processing 1

Descriptive Answer Evaluation System using Natural Language ...

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1st international conference on Cognitive Computing and engineering Education, ICCCEE 2023

作者： Nalavade, J.E. Kondiba Dabade, Tejashree MITADT School of Computing Department of Computer Science and Engineering Pune India Tata Consultancy Services Ltd India

ISBN: (纸本)9798350332803

In the education system, written exams play a vital role to test knowledge gained by students. But the total process from evaluating answers, grading, verification, and publishing results is time-consuming. A lot of human efforts are required for evaluating descriptive answers. This evaluation also depends on various factors like knowledge of the evaluator, application-level understanding of the evaluator. When any human being performs evaluation it varies along with the emotions of that person. So the goal of this paper is to develop the descriptive answer evaluation system which ensures uniformity in marking, saves a lot of time, and trouble of checking bundles of papers. In this evaluation system, first pre-processing techniques are applied to student's answers. Then answers are matched against manually entered model answer using different techniques like keyword matching, Bert and finally score is calculated. © 2023 IEEE.

关键词： natural language processing systems

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Advancements in natural language Understanding and Applications: A Study on ChatGPT with Novel Approaches to Fairness and Accuracy in Education 4

Advancements in Natural Language Understanding and Applicati...

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4th international Signal processing, Communications and engineering Management conference, ISPCEM 2024

作者： Guo, Siyuan Johns Hopkins University 3501 SAINT PAUL ST APT 836 Baltimore BaltimoreMD21218-2742 United States

ISBN: (纸本)9798331528676

natural language processing (NLP) has evolved significantly since the 1950s, with early research focusing on tasks like machine translation, information retrieval, and text summarization. Initially, most NLP research emphasized syntactic analysis, but the challenge of understanding and processing unstructured text remained. Recent advancements in large-scale language models (LLMs), such as ChatGPT, have revolutionized the field by enabling zero-shot execution of various NLP tasks without task-specific training. ChatGPT, based on the Transformer architecture, represents a major breakthrough in NLP, generating high-quality natural language responses and self-correcting based on dialogue context. This paper investigates the applications of ChatGPT in educational tasks, focusing on enhancing the fairness and accuracy of its responses. Fairness refers to ensuring that users receive consistent and unbiased answers to the same queries, while accuracy involves improving the precision of ChatGPT's outputs, particularly in complex educational tasks such as scientific literature review and content comprehension. We propose novel solutions, including dynamic model weighting for fairness and integrating external knowledge bases to boost accuracy. Experiments conducted on educational tasks demonstrate significant improvements in both fairness and accuracy, showing that the proposed methods enable ChatGPT to provide more reliable and consistent educational support. Our findings suggest that, with these innovations, ChatGPT can play a pivotal role in improving educational outcomes by offering fair access to quality learning content while addressing accuracy challenges. Future work will explore expanding this approach to other fields where NLP plays a crucial role. ©2024 IEEE.

关键词： Zero-shot learning

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