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AI-based Question Answering Assistance for Analyzing natural-language Requirements 23

AI-based Question Answering Assistance for Analyzing Natural...

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45th IEEE/ACM international conference on Software engineering (ICSE)

作者： Ezzini, Saad Abualhaija, Sallam Arora, Chetan Sabetzadeh, Mehrdad Univ Luxembourg SnT Ctr Secur Reliabil & Trust Luxembourg Luxembourg Deakin Univ Geelong Australia Monash Univ Victoria Australia Univ Ottawa Sch Elect Engn & Comp Sci Ottawa ON Canada

ISBN: (纸本)9781665457019

By virtue of being prevalently written in natural language (NL), requirements are prone to various defects, e.g., inconsistency and incompleteness. As such, requirements are frequently subject to quality assurance processes. These processes, when carried out entirely manually, are tedious and may further overlook important quality issues due to time and budget pressures. In this paper, we propose QAssist - a question-answering (QA) approach that provides automated assistance to stakeholders, including requirements engineers, during the analysis of NL requirements. Posing a question and getting an instant answer is beneficial in various quality-assurance scenarios, e.g., incompleteness detection. Answering requirements-related questions automatically is challenging since the scope of the search for answers can go beyond the given requirements specification. To that end, QAssist provides support for mining external domain-knowledge resources. Our work is one of the first initiatives to bring together QA and external domain knowledge for addressing requirements engineering challenges. We evaluate QAssist on a dataset covering three application domains and containing a total of 387 question-answer pairs. We experiment with state-of-the-art QA methods, based primarily on recent large-scale language models. In our empirical study, QAssist localizes the answer to a question to three passages within the requirements specification and within the external domain-knowledge resource with an average recall of 90.1% and 96.5%, respectively. QAssist extracts the actual answer to the posed question with an average accuracy of 84.2%.

关键词： natural-language Requirements Question Answering (QA) language Models natural language processing (NLP) natural language Generation (NLG) BERT T5

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Multimodal Learning in natural language processing: Fact-Checking, Ethics and Misinformation Detection

Multimodal Learning in Natural Language Processing: Fact-Che...

引用

2025 international conference on Multi-Agent Systems for Collaborative Intelligence, ICMSCI 2025

作者： Joshi, Himanshu Sanjay Taherdoost, Hamed College of Business Westcliff University CA United States College of Technology & Engineering Westcliff University CA United States University Canada West Vancouver Canada Gus Institute Global University Systems London United Kingdom Q Minded Quark Minded Technology Inc. Vancouver Canada

ISBN: (纸本)9798331509828

Multimodal learning (MML) is a subtype of deep learning that improves the capabilities of machine learning models by integrating modalities from multiple data sources such as text, images, audio, and video. This integration of modalities produces a more comprehensive understanding of complex data and nuanced outputs. Applications of this approach in real-time decision-making, information retrieval, and knowledge representation can improve the insights derived from the natural language processing (NLP) system. Moreover, in a time of information overload, there are concerns regarding the credibility of the content. The role of NLP systems is central to processing textual data in MML systems. In this environment, the ethical development of multimodal systems for fact-checking is crucial. This paper addresses an existing gap in use of multimodal learning in the context of fact-checking by investigating issues related to integration of NLP, Question-Answering (QA) systems, and knowledge graphs to combat misinformation. This research presents multimodal data based frameworks based on knowledge graph integration. The exploration of the frameworks enables to enhance the accuracy, reliability, and ethical soundness of fact-checking mechanisms. This study also highlights new directions for overcoming challenges related to modality alignment, computational efficiency, and ethical transparency. © 2025 IEEE.

关键词： knowledge graph

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Enhancing Misinformation Detection through Semantic Analysis and knowledge Graphs 4

Enhancing Misinformation Detection through Semantic Analysis...

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4th IEEE international conference on Data engineering and Communication Systems, ICDECS 2024

作者： Pillai, Sanjaikanth E Vadakkethil Somanathan University of North Dakota School of Electrical Engineering and Computer Science Grand Forks United States

ISBN: (纸本)9798350393354

Introducing improvements in technology has caused an exponential increase in the availability and consumption of virtual data. This has also given rise to the spread of misinformation, a problem that has significant consequences for both individuals and society. The ability to correctly locate and combat misinformation is vital in preventing its dangerous outcomes. Traditional methods of detecting misinformation, including truth-checking and source evaluation, have proven to be inadequate in the contemporary digital landscape. To address this problem, researchers have turned to advanced technology, including natural language processing and machine learning, to improve the accuracy and performance of misinformation detection. However, these techniques often depend on surface-level features, such as language patterns and metadata, which can be easily manipulated by those creating and disseminating misinformation. This quandary requires a deeper understanding of the underlying semantic meaning of the facts. Semantic analysis, a branch of natural language processing, focuses on understanding the meaning and context of language. By incorporating this method into misinformation detection, it is possible to go beyond surface-level features and analyze the underlying intent and meaning of the information. This could offer a much better and accurate approach for detecting misinformation. Moreover, the use of knowledge graphs can further enhance this approach by capturing and representing the relationships between entities, ideas, and information. © 2024 IEEE.

关键词： knowledge graph

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A comparative study of different BERT modifications

A comparative study of different BERT modifications

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international conference on Artificial Intelligence, Blockchain, Computing and Security, ICABCS 2023

作者： Agarwal, S. Jain, M. Department of Computer Science and Engineering Delhi Technological University Delhi India

ISBN: (纸本)9781032669663

natural language Understanding is a component of natural language processing that deals with how computers comprehend and understand text from human languages like English, German, and French etc. It enables machines to communicate back and is crucial for NLP applications like question answering, text summarization, chatbots etc. Transformers like BERT and GPT completely turned the tide with their unparalleled results in these applications but over time more and more drawbacks started surfacing for these transformers like high computational cost, large memory requirements, skipping masked tokens, lack of linguistic and dependent knowledge in the learned representations etc. This paper presents a brief view of some of these problems and how new technologies are trying to solve them. We have reviewed and summarized some models that have been quite effective in tackling the above mentioned problems of BERT. © 2024 The Author(s).

关键词： natural language processing systems

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Comparison of different natural language processing models to achieve semantic interoperability of heterogeneous asset administration shells 21

Comparison of different natural language processing models t...

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IEEE 21st international conference on Industrial Informatics (INDIN)

作者： Beermann, Jo Benfer, Rebekka Both, Maximilian Mueller, Jochen Diedrich, Christian TH Koln Fac Proc Engn Energy & Mech Syst Cologne Germany Otto von Guericke Univ Inst Automat Technol Magdeburg Germany

ISBN: (纸本)9781665493130

Self-organizing systems represent the next level of building automation and make it possible to reduce the manual engineering effort of automation systems. For self-organizing systems to be able to interact interoperable, the system components must be mapped by uniform digital twins and described in a semantically interoperable manner. Semantic interoperability is implemented in the current research approach of Industrie 4.0 through homogeneous semantics. However, given the large number of different manufacturers of technical components, agreement on uniform semantics seems unlikely. This paper presents a method that extends the Industrie 4.0 approach to heterogeneous semantics. Semantic interoperability is realized through the automated mapping of heterogeneous vocabularies to target semantics. Models from the artificial intelligence sub-field natural language processing are used for automated mapping. In this paper, existing models of natural language processing are compared with each other in terms of their mapping accuracy. A dataset based on the ECLASS standard is being developed as a basis for the comparison. This dataset is also being used to create new models that are fine-tuned to the target vocabulary. The results show that the mapping accuracy of existing approaches improves through fine-tuning by an average of 7.5% up to 93%. In addition to the improvement through fine-tuning, this work analyses the influence of the model size on the mapping accuracy by using large language models. Moreover, it examines the integration of structured knowledge in the form of knowledge graphs.

关键词： semantic interoperability natural language processing Industrie 4.0

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Systematic TextRank Optimization in Extractive Summarization

Systematic TextRank Optimization in Extractive Summarization

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2023 international conference Recent Advances in natural language processing: Large language Models for natural language processing, RANLP 2023

作者： Zieve, Morris Gregor, Anthony Stokbaek, Frederik Juul Lewis, Hunter Mendoza, Ellis Marie Ahmadnia, Benyamin Department of Computer Engineering and Computer Science California State University Long Beach United States

ISBN: (纸本)9789544520922

With the ever-growing amount of textual data, extractive summarization has become increasingly crucial for efficiently processing information. The TextRank algorithm, a popular unsupervised method, offers excellent potential for this task. In this paper, we aim to optimize the performance of TextRank by systematically exploring and verifying the best preprocessing and fine-tuning techniques. We extensively evaluate text preprocessing methods, such as tokenization, stemming, and stopword removal, to identify the most effective combination with TextRank. Additionally, we examine fine-tuning strategies, including parameter optimization and incorporation of domain-specific knowledge, to achieve superior summarization quality. © 2023 Incoma Ltd. All rights reserved.

关键词： Domain knowledge

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GraphLingo: Domain knowledge Exploration by Synchronizing knowledge Graphs and Large language Models 40

GraphLingo: Domain Knowledge Exploration by Synchronizing Kn...

引用

40th IEEE international conference on Data engineering, ICDE 2024

作者： Le, Duy Zhao, Kris Wang, Mengying Wu, Yinghui Case Western Reserve Univ. United States

ISBN: (纸本)9798350317152

knowledge graphs (KGs) are routinely curated to provide factual data for various domain-specific analyses. Nevertheless, it remains nontrivial to explore domain knowledge with standard query languages. We demonstrate GraphLingo, a natural language (NL)-based knowledge exploration system designed for exploring domain-specific knowledge graphs. It differs from conventional knowledge graph search tools in that it enables an interactive exploratory NL query over domain-specific knowledge graphs. GraphLingo seamlessly integrates graph query processing and large language models with a graph pattern-based prompt generation approach to guide users in exploring relevant factual knowledge. It streamlines NL-based question & answer, graph query optimization & refining, and automatic prompt generation. A unique feature of GraphLingo is its capability to enable users to explore by seamlessly switching between a more 'open' approach and a more relevant yet 'conservative' one, facilitated by diversified query suggestions. We show cases of GraphLingo in curriculum suggestion, and materials scientific data search. © 2024 IEEE.

关键词： knowledge graph

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Factoid Question Answering System using knowledge Graph 7

Factoid Question Answering System using Knowledge Graph

引用

2023 7th international conference On Computing, Communication, Control And Automation, ICCUBEA 2023

作者： Gaikwad, Vikas Patil, Archana College of Engineering Dept. of Computer Engineering and It Pune India

ISBN: (纸本)9798350304268

Here we discusses various topics related to natural language processing and question answering systems. It explains the difference between reading comprehension QA and knowledge Graph Question Answering (KGQA) and highlights the importance of world knowledge for dialogue systems. The text then goes on to describe a simple question answering system and the limitations of such systems in handling complex questions. Finally, the concept of knowledge graphs is introduced and their role in NLP applications is discussed. Overall, the text provides an overview of key concepts and techniques used in question answering systems and their potential applications in various fields. © 2023 IEEE.

关键词： Dialogue Systems knowledge Graph natural language processing Question Answering System

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LLMR: knowledge Distillation with a Large language Model-Induced Reward 30

LLMR: Knowledge Distillation with a Large Language Model-Ind...

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Joint 30th international conference on Computational Linguistics and 14th international conference on language Resources and Evaluation, LREC-COLING 2024

作者： Li, Dongheng Hao, Yongchang Mou, Lili University of Alberta Canada Canada CIFAR AI Amii Canada

ISBN: (纸本)9782493814104

Large language models have become increasingly popular and demonstrated remarkable performance in various natural language processing (NLP) tasks. However, these models are typically computationally expensive and difficult to be deployed in resource-constrained environments. In this paper, we propose LLMR, a novel knowledge distillation (KD) method based on a reward function induced from large language models. We conducted experiments on multiple datasets in the dialogue generation and summarization tasks. Empirical results demonstrate that our LLMR approach consistently outperforms traditional KD methods on different tasks and datasets. © 2024 ELRA language Resource Association: CC BY-NC 4.0.

关键词： Distillation

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natural language Interface for 3D Symbology: An Initial Design and Application to Utility Networks 19

Natural Language Interface for 3D Symbology: An Initial Desi...

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19th 3D GeoInfo conference / 31st EG-ICE international Workshop on Intelligent Computing in engineering

作者： Wang, Chen Gao, Xin-chang Li, Xin-yong Xie, Yi-ding Anhui Univ Anhui Prov Key Lab Wetland Ecosyst Protect & Rest Hefei 230601 Peoples R China Anhui Univ Sch Resources & Environm Engn Hefei 230601 Peoples R China

The growing adoption of digital twins in geomatics sectors requires efficient 3D mapping techniques. However, the complexity and cost of producing cartographically enriched 3D scenes pose significant challenges, hindering widespread application, particularly in domains with limited mapping expertise and budgets. The proposed methodology in this paper leverages recent advances in natural language processing and artificial intelligence, particularly large language models (LLMs), to reduce the expertise required for 3D mapping and to address the high costs and complexity associated with traditional cartographic processes. It introduces a natural language interface for 3D symbology, aimed at simplifying the design and automating the creation of cartographically enriched 3D scene. By allowing cartographers converse with the mapping system, the system translates verbal descriptions into structured symbology rules in 3D digital cartographic model, which are then used to generate cartographically enriched 3D scenes. The method chains multiple ad-hoc LLM-based agents for entity linking, conversation handling, and symbology rule verification. Prompt engineering methods, such as chain-of-though and retrieval augmented generation, have been used to guide the agents' reasoning process or leverage knowledge base, respectively. Experiment and application in utility networks demonstrates the method's capability to accurately interpret and execute 3D symbology rules from natural language inputs, resulting in cartographically enriched 3D scenes that are reproducible and scalable. This work represents a pioneer study to implement a natural language interface for 3D mapping. It not only enhances the usability and accessibility of 3D mapping in digital twins but also sets a foundational method for future research in natural language-based mapping interfaces.

关键词： 3D cartography Symbology natural language processing Large language Model Artificial Intelligence

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