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15th international conference on knowledge Discovery and Information Retrieval, KDIR 2023 as part of IC3K 2023 - Proceedings of the 15th international Joint conference on knowledge Discovery, knowledge engineering and knowledge Management

15th International Conference on Knowledge Discovery and Inf...

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15th international conference on knowledge Discovery and Information Retrieval, KDIR 2023 as part of the 15th international Joint conference on knowledge Discovery, knowledge engineering and knowledge Management, IC3K 2023

ISBN: (纸本)9789897586712

The proceedings contain 51 papers. The topics discussed include: identifying similar top-k household electricity consumption patterns;detecting greenwashing in the environmental, social, and governance domains using natural language processing;speech detection of real-time MRI vocal tract data;a flexible approach for retrieving geometrically similar finite element models using point cloud autoencoders;fine-tuning and aligning question answering models for complex information extraction tasks;closeness centrality detection in homogeneous multilayer networks;subject classification of software repository;recommendation system for product test failures using BERT;and an explainable knowledge graph-based news recommendation system.

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The method of utilizing prompt engineering to train large language models for the intelligent expression of affair-elements 11

The method of utilizing prompt engineering to train large la...

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11th international conference on Information Technology and Quantitative Management, ITQM 2024

作者： Liu, Zequan Wang, Longhao Li, Xingsen School of Electromechanical Engineering Guangdong University of Technology Guangzhou510006 China Institute of Extenics and Innovation Methods Guangdong University of Technology Guangzhou510006 China

This paper proposes the concept of applying large language models to the expression of extenics affair-elements with the aim of enhancing the accuracy and standardisation of computerised expression of extenics affair-elements. The feasibility of using prompt engineering to train large language models for intelligent expression of affair-elements is also verified. Firstly, the concepts and structured expressions of extenics and affair-elements are inputted into the large language model. Secondly, a training method for prompt engineering is designed, integrating few-shot prompts, thought-chain prompts, and system-2-attention prompts. Then, the feedback from the large language model's expression of affair-elements is evaluated in terms of standardisation and accuracy, and the prompts are continuously modified to interact with the large language model for iterative learning in expressing affair-elements. Finally, through the use of the case study of Kimi, the capacity of the large language model to express extenics affair-elements in a standardised manner following training is evaluated, thereby confirming the feasibility of this method. © 2024 The Authors.

关键词： natural language processing systems

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Can Large language Models Automatically Score Proficiency of Written Essays? 30

Can Large Language Models Automatically Score Proficiency of...

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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： Mansour, Watheq Albatarni, Salam Eltanbouly, Sohaila Elsayed, Tamer The University of Queensland Brisbane Australia Computer Science and Engineering Department Qatar University Doha Qatar

ISBN: (纸本)9782493814104

Although several methods were proposed to address the problem of automated essay scoring (AES) in the last 50 years, there is still much to desire in terms of effectiveness. Large language Models (LLMs) are transformer-based models that demonstrate extraordinary capabilities on various tasks. In this paper, we test the ability of LLMs, given their powerful linguistic knowledge, to analyze and effectively score written essays. We experimented with two popular LLMs, namely ChatGPT and Llama. We aim to check if these models can do this task and, if so, how their performance is positioned among the state-of-the-art (SOTA) models across two levels, holistically and per individual writing trait. We utilized prompt-engineering tactics in designing four different prompts to bring their maximum potential to this task. Our experiments conducted on the ASAP dataset revealed several interesting observations. First, choosing the right prompt depends highly on the model and nature of the task. Second, the two LLMs exhibited comparable average performance in AES, with a slight advantage for ChatGPT. Finally, despite the performance gap between the two LLMs and SOTA models in terms of predictions, they provide feedback to enhance the quality of the essays, which can potentially help both teachers and students. © 2024 ELRA language Resource Association: CC BY-NC 4.0.

关键词： natural language processing systems

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2nd international conference on Speech and language Technologies for Low-Resource languages, SPELLL 2023

2nd International Conference on Speech and Language Technolo...

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2nd international conference on Speech and language Technologies for Low-Resource languages, SPELLL 2023

ISBN: (纸本)9783031584947

The proceedings contain 33 papers. The special focus in this conference is on Speech and language Technologies for Low-resource languages. The topics include: SamPar: A Marathi Hate Speech Dataset for Homophobia, Transphobia;l3Cube-MahaNews: News-Based Short Text and Long Document Classification Datasets in Marathi;creation and Classification of Kannada Meme Dataset: Exploring Domain and Troll Categories;the Impact of Tamil Python Programming in Wikisource;natural language processing for Tulu: Challenges, Review and Future Scope;depBoost-TransNet: Boosted Transformer Network for Depression Classification;Optimized BERT Model for Question Answering System on Mobile Platform;A Comparative Analysis of Pretrained Models for Sentiment Analysis on Restaurant Customer Reviews (CAPM-SARCR);lightweight language Agnostic Data Sanitization Pipeline for Dealing with Homoglyphs in Code-Mixed languages;textGram: Towards a Better Domain-Adaptive Pretraining;abusive Social Media Comments Detection for Tamil and Telugu;sales Forecasting from Group Conversation Using natural language processing;hands in Harmony: Empowering Communication Through Translation;offensive Text Detection for Tamil language;Telugu-English Abusive Comment Detection Using XLMRoBERTa and mBERT;a knowledge engineering Framework Addressing High Incidence of Farmer Suicides;event Categorization from News Articles Using Machine Learning Techniques;from Words to Emotions: Identifying Depression Through Social Media Insights;text Summarisation for Low-Resourced languages, A Review;multi Speaker Activity Detection Using Spectral Centroids;spoken language Identification System Using Convolutional Neural Networks;exploring the Role of Entropy in Music Classification;spectral Features Based Spoken Dialect Identification for Punjabi language;hate Speech Detection Using Audio in Portuguese language;an Empirical Analysis of the Consonantal Phonemic Patterns and Characteristics of English Spoken in India;Multilingual Fake Ne

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Word Sense Disambiguation from English to Indic language: Approaches and Opportunities 4th

Word Sense Disambiguation from English to Indic Language: Ap...

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4th international conference on Soft Computing and its engineering Applications (icSoftComp)

作者： Mishra, Binod Kumar Jain, Suresh Medi Caps Univ Comp Sci & Engn Dept Indore India

ISBN: (纸本)9783031276088;9783031276095

Ambiguity is one of the major challenges in natural language processing and the process to solve is known as Word Sense Disambiguation. It is useful to determine the appropriate meaning of polysemy words in a given context using computational methods. Generally, knowledge, Supervised, and Unsupervised based approaches are the most common methods used to resolve ambiguity problems that occur in a sentence. The government of India has initiated many digital services for its citizen in the last decade. All these services require natural language processing to be easily accessed by web portals or any electronic gadget. Also, these services are provided by the government in Hindi or other Indian languages to better serve Indian citizens. Since English and other languages like Chinese, Japanese, and Korean have plenty of resources available to build applications based on natural language processing but due to low resources available for disambiguating polysemous words in Hindi and other Indian languages, it becomes a hindrance to building any application based on these languages. In this paper, the suggested method enables the assessment of the correct meaning in terms of sustaining data sequences. In order to automatically extract features, the proposed method uses an RNN neural network model. Additionally, it integrates glosses from IndoWordNet. The outcomes demonstrate that the suggested technique performs consistently and significantly better than the alternatives.

关键词： Word sense disambiguation RNN WordNet LESK Naive Bayes

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Detecting Health Misinformation on Social Networking Sites Using Large language Models and Deep Learning-based natural language processing 2

Detecting Health Misinformation on Social Networking Sites U...

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2nd international conference on Foundation and Large language Models, FLLM 2024

作者： Alshahrani, Amenah Misfer Farooq Ahmad, Hafiz Hussain, Jamil King Faisal University College of Computer Sciences and Information Technology Department of Computer Science Al-Ahsa Saudi Arabia Sejong University Department of Computer Science Seoul Korea Republic of

ISBN: (纸本)9798350354799

Health misinformation on social networking sites (SNS) is a critical issue, particularly during health crises like the COVID-19 pandemic. The spread of inaccurate health information can lead to severe outcomes, including reduced vaccine uptake and decreased trust in healthcare. This paper introduces a system using large language models (LLMs) and deep learning-based natural language processing (NLP) techniques to detect and mitigate health misinformation on SNS. The model incorporates transformer-based architectures, zero-shot (ZS) and few-shot (FS) learning, and prompt engineering to classify health-related content as true or false. A key feature is the integration of a knowledge graph that enhances verification capabilities. Applied to both English and Arabic datasets, the system's performance is evaluated using accuracy, F1-score, BLEU, and METEOR metrics, demonstrating its multilingual effectiveness in real-time misinformation detection. © 2024 IEEE.

关键词： Electronic health record

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From Monolingual to Multilingual: Enhancing Hate Speech Detection with Multi-channel language Models 28th

From Monolingual to Multilingual: Enhancing Hate Speech Dete...

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28th international conference on knowledge Based and Intelligent information and engineering Systems, KES 2024

作者： Kia, Mahsa Abazari Samiee, Dorsa Northeastern University London United Kingdom Royal Holloway University of London United Kingdom

The rise of social networking services (SNS) has reshaped communication dynamics in cyberspace, yet it has also exacerbated the proliferation of online hate speech due to the anonymity and fluidity these platforms offer. With manual hate speech detection by human annotators proving costly and time-intensive, the imperative for developing automated recognition algorithms is evident. Leveraging knowledge transfer through fine-tuning pre-trained language models has emerged as a promising strategy in natural language processing. Notably, Bidirectional Encoder Representations from Transformers (BERT) and HateBERT, a domain-specific variant, stand out for their ability to glean deep bidirectional representations from extensive corpora. This paper introduces a multichannel model integrating these two language models for multilingual hate speech detection. Furthermore, we explore the efficacy of augmenting input data through translation, ensuring compatibility with the English requirement of the HateBERT model. By evaluating our approach on three non-English datasets alongside an English dataset, we demonstrate achieving state-of-the-art or comparable performance, underscoring the effectiveness of our methodology in combating hate speech across linguistic barriers. © 2024 The Authors.

关键词： Deep learning

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Transcription of American Sign language (ASL) Using Convolutional Neural Networks (CNNs) 2nd

Transcription of American Sign Language (ASL) Using Convolut...

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2nd international conference on Women Researchers in Electronics and Computing, WREC 2023

作者： Chaudhary, Nishu Sheel, Ridham Kumar, Ranjan Mittal, Poornima Delhi Technological University Delhi India

ISBN: (纸本)9789819970766

Sign language is most natural and the fastest way to gather knowledge and information, and it is an expressive and efficient way for the people with hearing and speech impairment to convey their thoughts and feelings. Sign language to text conversion technology allows these individuals to more easily communicate with others by converting their hand gestures into text. This technology can bridge the communication gap and improve integration and accessibility for people who rely and primarily utilize sign language as their prior means of expression. This translation is done using image processing, computer vision and machine learning technique. This technology can process video input of American hand signs and convert them into text in more than one language, providing real-time captioning and subtitle improving communication and accessibility for people with HSD and with our method we get 95.7% accuracy for 26 letters. The technology not only helps people with hearing and speech impairments, but also helps in teaching sign language to those who are not familiar with it. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Audition

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BERT Transformers Multitask Learning Sarcasm and Sentiment Classification (BMSS) 13

BERT Transformers Multitask Learning Sarcasm and Sentiment C...

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13th international conference on Computer and knowledge engineering, ICCKE 2023

作者： Molavi, Fateme Mohasefi, Jamshid Bagherzadeh Urmia Univercity Tehran Iran Urmia University Part Computer Engineering Urmia Iran

ISBN: (纸本)9798350330151

Numerous researches were reported based on analyzing the sentiment of texts, which are becoming considerably vital to discover the sentiment in various domains of economic, marketing, politic, and etc. However, one of the most challenging aspects of natural language processing (NLP) is discovery of the sarcasm. The sarcasm and the sentiment as similar subjects have been explored in various articles. In this research, two significant achievements were employed. Firstly, the pre-trained BERT transformer model was used to transfer its knowledge to our model. Then, fine tuned BERT model was combined with a machine learning approach, specifically Multitask learning (MTL). By establishing a connection between sarcasm and sentiment, we observed a simultaneous enhancement in both sarcasm detection and sentiment analysis performance. The achieved accuracy results (average of 0.97) were deemed satisfactory and commendable for both tasks. © 2023 IEEE.

关键词： Sentiment analysis

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BAMGAN: A KBQA method based GAN and bi-directional attention 6

BAMGAN: A KBQA method based GAN and bi-directional attention

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6th international conference on Advanced Electronic Materials, Computers, and Software engineering, AEMCSE 2023

作者： Guo, Jiazhi Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming Yunnan650504 China

ISBN: (纸本)9781510668249

knowledge-based question answering(KBQA) involves using knowledge base technology to generate answers to natural language processing(NLP) questions. KBQA is one of the most challenging tasks in the field of NLP. Answer selection plays a vital role in KBQA, as it requires selecting the correct answer from a pool of candidate answers. However, knowledge bases still struggle to identify the correct answer from multiple candidate answers. To address this issue, In this paper, we propose BAMGAN, a KBQA method based on generative adversarial networks. This method uses generative adversarial networks (GANs) to improve answer selection accuracy in deep neural networks. Experimental results show the effectiveness of the BAMGAN. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Generative adversarial networks

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