Relevant bibliographies by topics / Natural language processing, question answering, software engineering

Academic literature on the topic 'Natural language processing, question answering, software engineering'

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Published: 6 September 2023

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Journal articles on the topic "Natural language processing, question answering, software engineering"

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Nabavi, Armin, Issa Ramaji, Naimeh Sadeghi, and Anne Anderson. "Leveraging Natural Language Processing for Automated Information Inquiry from Building Information Models." Journal of Information Technology in Construction 28 (April 4, 2023): 266–85. http://dx.doi.org/10.36680/j.itcon.2023.013.

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Building Information Modeling (BIM) is a trending technology in the building industry that can increase efficiency throughout construction. Various practical information can be obtained from BIM models during the project life cycle. However, accessing this information could be tedious and time-consuming for non-technical users, who might have limited or no knowledge of working with BIM software. Automating the information inquiry process can potentially address this need. This research proposes an Artificial Intelligence-based framework to facilitate accessing information in BIM models. First, the framework uses a support vector machine (SVM) algorithm to determine the user's question type. Simultaneously, it employs natural language processing (NLP) for syntactic analysis to find the main keywords of the user's question. Then it utilizes an ontology database such as IfcOWL and an NLP method (latent semantic analysis (LSA)) for a semantic understanding of the question. The keywords are expanded through the semantic relationship in the ontologies, and eventually, a final query is formed based on keywords and their expanded concepts. A Navisworks API is developed that employs the identified question type and its parameters to extract the results from BIM and display them to the users. The proposed platform also includes a speech recognition module for a more user-friendly interface. The results show that the speed of answering the questions on the platform is up to 5 times faster than the manual use by experts while maintaining high accuracy.
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Goar, Vishal, Nagendra Singh Yadav, and Pallavi Singh Yadav. "Conversational AI for Natural Language Processing: An Review of ChatGPT." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 3s (March 11, 2023): 109–17. http://dx.doi.org/10.17762/ijritcc.v11i3s.6161.

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ChatGPT is a conversational artificial intelligence model developed by OpenAI, which was introduced in 2019. It employs a transformer-based neural mesh to produce human being responses in real-time, allowing for natural language conversations with a machine. ChatGPT is instructed on huge quantities of data captured using the internet, making it knowledgeable in an extensive span of topics, from news & entertainment to politics and sports. This allows it to generate contextually relevant responses to questions and statements, making the conversation seem more lifelike. The model can be used in various applications, including customer service, personal assistants, and virtual assistants. ChatGPT has also shown promising results in generating creative content, such as jokes and poetry, showcasing its versatility and potential for future applications.This paper provides a comprehensive review of the existing literature on ChatGPT, highlighting its key advantages, such as improved accuracy and flexibility compared to traditional NLP tools, as well as its limitations and the need for further research to address potential ethical concerns. The review also highlights the potential for ChatGPT to be used in NLP applications, including question-answering and dialogue generation, and highlights the need for further research and development in these areas.
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Mima, Hideki, Susumu Ota, and Koji Nagatsuna. "Ontology-based query processing for understanding intentions of indirect speech acts in natural-language question answering." International Journal of Computer Applications in Technology 35, no. 2/3/4 (2009): 271. http://dx.doi.org/10.1504/ijcat.2009.026603.

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Moholkar, Kavita, and S. H. Patil. "Lioness Adapted GWO-Based Deep Belief Network Enabled with Multiple Features for a Novel Question Answering System." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 30, no. 01 (February 2022): 93–114. http://dx.doi.org/10.1142/s0218488522500052.

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Recently, the researches on Question Answering (QA) systems attract progressive attention with the enlargement of data and the advances on machine learning. Selection of answers from QA system is a significant task for enhancing the automatic QA systems. However, the major complexity relies in the designing of contextual factors and semantic matching. Motivation: Question Answering is a specialized form of Information Retrieval which seeks knowledge. We are not only interested in getting the relevant pages but we are interested in getting specific answer to queries. Question Answering is in itself intersection of Natural Language Processing, Information Retrieval, Machine Learning, Knowledge Representation, Logic and Inference and Semantic Search. Contribution: Feature extraction plays a major role for accurate classification, where the learned features get extracted for enhancing the capability of sequence learning. Optimized Deep Belief network model is adopted for the precise question answering system, which could handle both objective and subjective questions. A new hybrid optimization algorithm known as Lioness Adapted GWO (LA-GWO) algorithm is introduced, which mainly concentrates on high reliability and convergence rate. This paper intends to formulate a novel QA system, and the process starts with word embedding. From the embedded results, some of the features get extracted, and subsequently, the classification is carried out using the hybrid optimization enabled Deep Belief Network (DBN). Specifically, the hidden neurons in DBN will be optimally tuned using a new Lioness Adapted GWO (LA-GWO) algorithm, which is the hybridization of both Lion Algorithm (LA) and Grey Wolf optimization (GWO) models. Finally, the performance of proposed work is compared over other conventional methods with respect to accuracy, sensitivity, specificity, and precision, respectively.
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Church, Kenneth Ward, Zeyu Chen, and Yanjun Ma. "Emerging trends: A gentle introduction to fine-tuning." Natural Language Engineering 27, no. 6 (October 26, 2021): 763–78. http://dx.doi.org/10.1017/s1351324921000322.

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AbstractThe previous Emerging Trends article (Church et al., 2021. Natural Language Engineering27(5), 631–645.) introduced deep nets to poets. Poets is an imperfect metaphor, intended as a gesture toward inclusion. The future for deep nets will benefit by reaching out to a broad audience of potential users, including people with little or no programming skills, and little interest in training models. That paper focused on inference, the use of pre-trained models, as is, without fine-tuning. The goal of this paper is to make fine-tuning more accessible to a broader audience. Since fine-tuning is more challenging than inference, the examples in this paper will require modest programming skills, as well as access to a GPU. Fine-tuning starts with a general purpose base (foundation) model and uses a small training set of labeled data to produce a model for a specific downstream application. There are many examples of fine-tuning in natural language processing (question answering (SQuAD) and GLUE benchmark), as well as vision and speech.
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Deshmukh, Prof Anushree, Smit Shah, Heena Puthran, and Naisargi Shah. "Virtual Shopping Assistant for Online Fashion Store." International Journal for Research in Applied Science and Engineering Technology 10, no. 5 (May 31, 2022): 110–17. http://dx.doi.org/10.22214/ijraset.2022.42099.

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Abstract: A new way for individuals to interact with computer systems will be done through chatbots or conversational interfaces. Historically, introducing a matter answered by a software package involves employing a program or filling out a type. The technology at the core of the increase of the chatbot is NLP i.e., Natural Language Processing. Sequence to Sequence (often abbreviated to seq2seq) fashions is a specific type of Recurrent Neural Network architectures that we commonly use (but no longer restricted) to clear up complicated Language issues like Machine Translation, Question Answering, growing Chatbots, Text Summarization, and so forth. Recent advances in machine learning have greatly improved the accuracy and effectiveness of NLP, creating chatbots a viable choice for several organizations like e-commerce, Customer service, Conversational apps, social media, Sales/Marketing/Branding, as Voice modules, Travel industry, Medicine, Hospitality, Human Resources etc. An NLP primarily based chatbot is a pc software or synthetic brain that communicates with a patron by means of textual or sound strategies This improvement in NLP is firing a great deal of additional analysis and research which should lead to continued growth in the effectiveness of chatbots in the years to come. Stochastic gradient descent (often abbreviated SGD) is an iterative approach for optimizing an goal characteristic with appropriate smoothness homes (e.g. differentiable or sub differentiable). Usage of Chatbots can also prove to be beneficial in ways like economically offering 24/7 service, improving customer satisfaction, reaching a younger demographic, reducing costs, increasing revenue and much more. Keywords: Chatbots, Natural Language Processing (NLP), Stochastic Gradient Descent (SGD), Sequential Model, Machine Learning
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Nguyen, Bao-An, and Don-Lin Yang. "A semi-automatic approach to construct Vietnamese ontology from online text." International Review of Research in Open and Distributed Learning 13, no. 5 (November 15, 2012): 148. http://dx.doi.org/10.19173/irrodl.v13i5.1250.

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An ontology is an effective formal representation of knowledge used commonly in artificial intelligence, semantic web, software engineering, and information retrieval. In open and distance learning, ontologies are used as knowledge bases for e-learning supplements, educational recommenders, and question answering systems that support students with much needed resources. In such systems, ontology construction is one of the most important phases. Since there are abundant documents on the Internet, useful learning materials can be acquired openly with the use of an ontology. However, due to the lack of system support for ontology construction, it is difficult to construct self-instructional materials for Vietnamese people. In general, the cost of manual acquisition of ontologies from domain documents and expert knowledge is too high. Therefore, we present a support system for Vietnamese ontology construction using pattern-based mechanisms to discover Vietnamese concepts and conceptual relations from Vietnamese text documents. In this system, we use the combination of statistics-based, data mining, and Vietnamese natural language processing methods to develop concept and conceptual relation extraction algorithms to discover knowledge from Vietnamese text documents. From the experiments, we show that our approach provides a feasible solution to build Vietnamese ontologies used for supporting systems in education.<br /><br />
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A, Hlybovets, and Tsaruk A. "Software architecture of the question-answering subsystem with elements of self-learning." Artificial Intelligence 26, jai2021.26(2) (December 1, 2021): 88–95. http://dx.doi.org/10.15407/jai2021.02.088.

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Within the framework of this paper, the analysis of software systems of question-answering type and their basic architectures has been carried out. With the development of machine learning technologies, creation of natural language processing (NLP) engines, as well as the rising popularity of virtual personal assistant programs that use the capabilities of speech synthesis (text-to-speech), there is a growing need in developing question-answering systems which can provide personalized answers to users' questions. All modern cloud providers proposed frameworks for organization of question answering systems but still we have a problem with personalized dialogs. Personalization is very important, it can put forward additional demands to a question-answering system’s capabilities to take this information into account while processing users’ questions. Traditionally, a question-answering system (QAS) is developed in the form of an application that contains a knowledge base and a user interface, which provides a user with answers to questions, and a means of interaction with an expert. In this article we analyze modern approaches to architecture development and try to build system from the building blocks that already exist on the market. Main criteria for the NLP modules were: support of the Ukrainian language, natural language understanding, functions of automatic definition of entities (attributes), ability to construct a dialogue flow, quality and completeness of documentation, API capabilities and integration with external systems, possibilities of external knowledge bases integration After provided analyses article propose the detailed architecture of the question-answering subsystem with elements of self-learning in the Ukrainian language. In the work you can find detailed description of main semantic components of the system (architecture components)
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Ali, Miss Aliya Anam Shoukat. "AI-Natural Language Processing (NLP)." International Journal for Research in Applied Science and Engineering Technology 9, no. VIII (August 10, 2021): 135–40. http://dx.doi.org/10.22214/ijraset.2021.37293.

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Natural Language Processing (NLP) could be a branch of Artificial Intelligence (AI) that allows machines to know the human language. Its goal is to form systems that can make sense of text and automatically perform tasks like translation, spell check, or topic classification. Natural language processing (NLP) has recently gained much attention for representing and analysing human language computationally. It's spread its applications in various fields like computational linguistics, email spam detection, information extraction, summarization, medical, and question answering etc. The goal of the Natural Language Processing is to style and build software system which will analyze, understand, and generate languages that humans use naturally, so as that you just could also be ready to address your computer as if you were addressing another person. Because it’s one amongst the oldest area of research in machine learning it’s employed in major fields like artificial intelligence speech recognition and text processing. Natural language processing has brought major breakthrough within the sector of COMPUTATION AND AI.
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Dong, Wei Jun, and Guo Hua Geng. "Research and Implementation of Intelligent Question Answering System in MOOC." Applied Mechanics and Materials 678 (October 2014): 639–43. http://dx.doi.org/10.4028/www.scientific.net/amm.678.639.

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Massive Online Open Course which based on Open Educational Resource might be the most effective method to large-scale quality education, which can realize passive learning to active learning. Analyzing the status and shortages of Intelligent Answering System, propose and design an intelligent question answering system based on agent-model. System use software agents to implement and improve MOOC system’s Intelligent Answering System performance, which has capacity of natural language processing, and good versatility. It can provide an efficient online problem answer environment for thousands of learners, and can effectively promote students' autonomous learning and self-development.
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Dissertations / Theses on the topic "Natural language processing, question answering, software engineering"

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Bergkvist, Alexander, Nils Hedberg, Sebastian Rollino, and Markus Sagen. "Surmize: An Online NLP System for Close-Domain Question-Answering and Summarization." Thesis, Uppsala universitet, Institutionen för informationsteknologi, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-412247.

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The amount of data available and consumed by people globally is growing. To reduce mental fatigue and increase the general ability to gain insight into complex texts or documents, we have developed an application to aid in this task. The application allows users to upload documents and ask domain-specific questions about them using our web application. A summarized version of each document is presented to the user, which could further facilitate their understanding of the document and guide them towards what types of questions could be relevant to ask. Our application allows users flexibility with the types of documents that can be processed, it is publicly available, stores no user data, and uses state-of-the-art models for its summaries and answers. The result is an application that yields near human-level intuition for answering questions in certain isolated cases, such as Wikipedia and news articles, as well as some scientific texts. The application shows a decrease in reliability and its prediction as to the complexity of the subject, the number of words in the document, and grammatical inconsistency in the questions increases. These are all aspects that can be improved further if used in production.
Mängden data som är tillgänglig och konsumeras av människor växer globalt. För att minska den mentala trötthet och öka den allmänna förmågan att få insikt i komplexa, massiva texter eller dokument, har vi utvecklat en applikation för att bistå i de uppgifterna. Applikationen tillåter användare att ladda upp dokument och fråga kontextspecifika frågor via vår webbapplikation. En sammanfattad version av varje dokument presenteras till användaren, vilket kan ytterligare förenkla förståelsen av ett dokument och vägleda dem mot vad som kan vara relevanta frågor att ställa. Vår applikation ger användare möjligheten att behandla olika typer av dokument, är tillgänglig för alla, sparar ingen personlig data, och använder de senaste modellerna inom språkbehandling för dess sammanfattningar och svar. Resultatet är en applikation som når en nära mänsklig intuition för vissa domäner och frågor, som exempelvis Wikipedia- och nyhetsartiklar, samt viss vetensaplig text. Noterade undantag för tillämpningen härrör från ämnets komplexitet, grammatiska korrekthet för frågorna och dokumentets längd. Dessa är områden som kan förbättras ytterligare om den används i produktionen.
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Book chapters on the topic "Natural language processing, question answering, software engineering"

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Papathomas, Evangelos, Themistoklis Diamantopoulos, and Andreas Symeonidis. "Semantic Code Search in Software Repositories using Neural Machine Translation." In Fundamental Approaches to Software Engineering, 225–44. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-99429-7_13.

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AbstractNowadays, software development is accelerated through the reuse of code snippets found online in question-answering platforms and software repositories. In order to be efficient, this process requires forming an appropriate query and identifying the most suitable code snippet, which can sometimes be challenging and particularly time-consuming. Over the last years, several code recommendation systems have been developed to offer a solution to this problem. Nevertheless, most of them recommend API calls or sequences instead of reusable code snippets. Furthermore, they do not employ architectures advanced enough to exploit the semantics of natural language and code in order to form the optimal query from the question posed. To overcome these issues, we propose CodeTransformer, a code recommendation system that provides useful, reusable code snippets extracted from open-source GitHub repositories. By employing a neural network architecture that comprises advanced attention mechanisms, our system effectively understands and models natural language queries and code snippets in a joint vector space. Upon evaluating CodeTransformer quantitatively against a similar system and qualitatively using a dataset from Stack Overflow, we conclude that our approach can recommend useful and reusable snippets to developers.
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Naik, Neelam Pramod. "Performance Measurement of Natural Dialog System by Analyzing the Conversation." In Advances in Systems Analysis, Software Engineering, and High Performance Computing, 180–209. IGI Global, 2022. http://dx.doi.org/10.4018/978-1-7998-9121-5.ch009.

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The natural dialog system, Chatbot, plays an important role in business domains by properly answering customer queries. In the pattern matching approach of Chatbot development, the user input is matched with a predefined set of responses. The machine learning approach of Chatbot development uses the principles of natural language processing to learn from conversational content. This study focuses on the performance measurement of pattern-based and machine learning-based Chatbot systems. As per the user point of view, performance measurement parameters are the ability to answer quickly, accurately, and comprehensively; to understand questions clearly; user friendliness; personalization options; ethics followed; and ability to process user feedback. The comprehensiveness of the knowledge base, robustness to handle unexpected input, scalability, and interoperability are some of the parameters considered to evaluate the Chatbot system by expert point of view. In this study, the specially designed Chatbot Usability Questionnaire is used to measure the performance of the implemented Chatbot systems.
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Wu, Finch C. T., Oscar N. J. Hong, Amy J. C. Trappey, and Charles V. Trappey. "VR-Enabled Chatbot System Supporting Transformer Mass-Customization Services." In Advances in Transdisciplinary Engineering. IOS Press, 2020. http://dx.doi.org/10.3233/atde200088.

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Chatbot is a conversational question answering (Q&A) system capable of natural language communication between a computer system and a person. The use of chatbots for 24-hour customer service provides quick responses that solve problems online. This approach is quickly becoming a convenient way for companies to enhance their customer services without location or knowledgeable staff limitations. This research proposes a system framework and develops a prototype virtual reality (VR) enabled transformer mass-customization consultation chatbot. The chatbot technique is a retrieval-based intelligent system. First, thousands of transformer specific frequently asked questions (FAQs) are collected as a Q&A dataset for technical supports retrieval. More than 1.2 million engineering Wikipedia pages and engineering technical papers are used to train a word embedding model used for natural language processing and question-answer retrieval. The chatbot is integrated into a virtual reality (VR) immersive user interface (UI) environment enabling users to make transformer design changes while querying the system about specifications and standards while interacting with 3D models from the company’s knowledge base archive. The system provides two unique UIs for personal computer (PC) and a helmet-based immersive interface. The system supports real-time consultation of mass-customized transformers and their bills of materials (BOM) for design review, analysis and cost estimation.
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Shirafuji, Atsushi, Takumi Ito, Makoto Morishita, Yuki Nakamura, Yusuke Oda, Jun Suzuki, and Yutaka Watanobe. "Prompt Sensitivity of Language Model for Solving Programming Problems." In Frontiers in Artificial Intelligence and Applications. IOS Press, 2022. http://dx.doi.org/10.3233/faia220264.

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A popular language model that can solve introductory programming problems, OpenAI’s Codex, has drawn much attention not only in the natural language processing field but also in the software engineering field. It supports programmers by suggesting the next tokens to write, and it can even generate a whole function definition from a document string. We focus on its capability of automatically solving programming problems through code generation from problem descriptions. We investigate the model’s sensitivity to problem descriptions by formatting and modifying them. The experimental results show that the more explicitly formatted problem description enhances the code generation performance from 30.9% (raw) to 39.9% (formatted). Additionally, we observe that code generation relies on information specified in the problem description, such as variable names and constant values, as anonymizing them reduces the performance significantly. Moreover, statistical biases in code generation are identified, such as the generated programs ignoring the problem modification and answering the exact opposite problem. The changes in accuracy across formats suggest that the model does not correctly understand the natural language explaining the problem specification even if the model could solve the programming problems with high accuracy.
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Duží, Marie. "‘Knowing-that’ vs. ‘Knowing-wh’." In Frontiers in Artificial Intelligence and Applications. IOS Press, 2023. http://dx.doi.org/10.3233/faia220498.

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Though there is a huge amount of the so-called epistemic logics that deal with propositional attitudes, i.e., sentences of the form “a knows that P”, their ‘wh-cousins’ of the form “a knows who is a P”, “a knows what the P is”, “a knows which Ps are Qs”, etc., have been, to the best of my knowledge, almost neglected. A similar disproportion can be observed between the analysis of Yes-No questions, which has been under scrutiny of many erotetic logics, and Wh-questions which have been dealt with just by a few authors. To fill this gap, we have analysed Wh-questions in Transparent Intensional Logic (TIL) and adjusted Gentzen’s system of natural deduction to TIL natural language processing; thus, our TIL question-answering system can answer not only Yes-No questions but also derive answers to Wh-questions. In this paper, I am going to apply these results to the analysis of sentences containing a ‘knowing-wh’ constituent. In addition, I will analyse the relation between ‘knowing-that’ and ‘knowing-wh’. For instance, if a knows that the Mayor of Ostrava is Mr Macura, can we logically derive that a knows who is the Mayor of Ostrava? Or, vice versa, if a knows who is the Mayor of Ostrava and the Mayor of Ostrava is Mr Macura, do these assumptions logically entail that a knows that the Mayor of Ostrava is Mr Macura? Though in case of rational human agents the answers seem to be a no-doubt YES, perhaps a rather surprising answer is in general negative. We have to specify rules for deriving the relation between knowing-that and knowing-wh, and if a software agent is rational but resource bounded, it does not have to have in its ontology the rules necessary to derive the answer. The goal of the paper is the specification of these rules. Hence, when applying these results into the design of a multi-agent system composed of software resource-bounded agents, we have to compute their inferable knowledge, which accounts not only for their explicit knowledge but also for their inferential abilities.
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Conference papers on the topic "Natural language processing, question answering, software engineering"

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Demner-Fushman, Dina. "Adapting naturally occurring test suites for evaluation of clinical question answering." In Software Engineering, Testing, and Quality Assurance for Natural Language Processing. Morristown, NJ, USA: Association for Computational Linguistics, 2008. http://dx.doi.org/10.3115/1622110.1622115.

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Arbaaeen, Ammar, and Asadullah Shah. "Natural Language Processing based Question Answering Techniques: A Survey." In 2020 IEEE 7th International Conference on Engineering Technologies and Applied Sciences (ICETAS). IEEE, 2020. http://dx.doi.org/10.1109/icetas51660.2020.9484290.

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Ezzini, Saad, Sallam Abualhaija, Chetan Arora, and Mehrdad Sabetzadeh. "AI-based Question Answering Assistance for Analyzing Natural-language Requirements." In 2023 IEEE/ACM 45th International Conference on Software Engineering (ICSE). IEEE, 2023. http://dx.doi.org/10.1109/icse48619.2023.00113.

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Sarker, Jaydeb, Mustain Billah, and Md Al Mamun. "Textual Question Answering for Semantic Parsing in Natural Language Processing." In 2019 1st International Conference on Advances in Science, Engineering and Robotics Technology (ICASERT). IEEE, 2019. http://dx.doi.org/10.1109/icasert.2019.8934734.

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Cao, Junkuo, and Xuanjing Huang. "Answering definitional question by dependency-based knowledge." In 2008 International Conference on Natural Language Processing and Knowledge Engineering (NLP-KE). IEEE, 2008. http://dx.doi.org/10.1109/nlpke.2008.4906809.

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Wen, Dunwei, Shen Jiang, and Yangjian He. "A question answering system based on VerbNet frames." In 2008 International Conference on Natural Language Processing and Knowledge Engineering (NLP-KE). IEEE, 2008. http://dx.doi.org/10.1109/nlpke.2008.4906769.

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Brini, Wissal, Mariem Ellouze, Slim Mesfar, and Lamia Hadrich Belguith. "An Arabic question-answering system for factoid questions." In 2009 International Conference on Natural Language Processing and Knowledge Engineering (NLP-KE). IEEE, 2009. http://dx.doi.org/10.1109/nlpke.2009.5313730.

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Yang, Ye, Song Liu, Shingo Kuroiwa, and Fuji Ren. "Question Answering System of Confucian Analects based on Pragmatics Information and Categories." In 2007 International Conference on Natural Language Processing and Knowledge Engineering. IEEE, 2007. http://dx.doi.org/10.1109/nlpke.2007.4368056.

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Jia, Keliang, Yongle Sun, and Zhinuo Li. "Answer extraction based on query expansion in Chinese question answering system." In 2008 International Conference on Natural Language Processing and Knowledge Engineering (NLP-KE). IEEE, 2008. http://dx.doi.org/10.1109/nlpke.2008.4906756.

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Ou, Shiyan, Dalila Mekhaldi, and Constantin Orasan. "An ontology-based question answering method with the use of textual entailment." In 2009 International Conference on Natural Language Processing and Knowledge Engineering (NLP-KE). IEEE, 2009. http://dx.doi.org/10.1109/nlpke.2009.5313770.

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