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检索条件"任意字段=International Conference on Natural Language Processing and Knowledge Engineering"

共 27256 条记录，以下是231-240 订阅

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Abusive Comment Detection from Bengali-English Code-Mixed Social Media Texts Using Ensemble of Deep Learning 4th

Abusive Comment Detection from Bengali-English Code-Mixed So...

引用

4th international conference on Artificial Intelligence and knowledge processing

作者： Fahim, Iftekhar Ahsan, Shawly Hoque, Mohammed Moshiul Chittagong Univ Engn & Technol Chattogram 4349 Bangladesh

ISBN: (纸本)9783031734762;9783031734779

Code-mixing, which involves seamlessly combining multiple languages within a single text, has become increasingly common on social media platforms. The pervasiveness of aggressive content and offensive language on social media presents significant challenges, necessitating the development of automatic detection methods. This problem becomes more complex when dealing with code-mixed text owing to the cultural nuances of different languages. Although efforts to identify abusive content in code-mixed text have primarily concentrated on high-resource languages, research on resource-constrained languages, such as Bengali mixed with English, still needs to be completed. Some studies have aimed at detecting abusive content in transliterated Bengali texts. However, there is a notable absence of research addressing the detection of abusive content in Bengali-English code-mixed texts. To address this gap, this paper presents a custom-built Bengali-English code-mixed dataset containing 2700 annotated comments categorized as abusive and non-abusive. To facilitate research in this area, this work proposes an ensemble of deep learning (DL) models: CNN (using GloVe embeddings), LSTM (implemented with Keras), and BiLSTM (utilizing FastText embeddings). The ensemble approach attained the most elevated weighted f1-score of 0.81. This research aims to tackle the growing issue of abusive content in code-mixed data, creating safer and more inclusive online environments.

关键词： natural language processing Code-mixing Deep learning Text processing Abusive content detection

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Next timestamp prediction in business process monitoring using large language models 2

Next timestamp prediction in business process monitoring usi...

引用

2nd international conference on Big Data, Computational Intelligence, and Applications, BDCIA 2024

作者： Xu, Yifei Fang, Huan School of Mathematics and Big Data Anhui University of Science and Technology Anhui Huainan China Anhui Province Engineering Laboratory for Big Data Analysis and Early Warning Technology of Coal Mine Safety Anhui Huainan China

ISBN: (纸本)9781510689053

Predictive business process monitoring is a critical domain within process mining, with the primary goal of forecasting characteristics of the next event or the remaining time for process executions. Large language models (LLMs) such as Generative Pretrained Transformer (GPT) and Tongyi Qianwen (Qwen) have demonstrated exceptional performance in natural language processing but face challenges when applied to process data prediction. This paper introduces LLM4NT, which leverages the decoder component of the Qwen2.5-0.5B model as its backbone. By employing a multi-head cross-attention mechanism, it reprograms process data and textual information, aligning process and text modalities to achieve precise predictions of the next timestamp in predictive monitoring. Experimental results on multiple real-life event logs demonstrate that LLM4NT achieves state-of-the-art average performance in next-timestamp prediction tasks, showcasing its powerful capabilities in business process reasoning. © 2025 SPIE.

关键词： Deep neural networks

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Using Machine Learning to Enhance Personalization in E-Learning

Using Machine Learning to Enhance Personalization in E-Learn...

引用

2025 international conference on Pervasive Computational Technologies, ICPCT 2025

作者： Palimkar, Sneha S. Kunnathully, Kannan Thota, Srinivasarao Joshi, Meeta Imran Akkalkot, Afsha Al Said, Nidal Coep Technological University Computer Engineering and Technology Maharashtra Pune India Veltech School of Law India Capital One Software Engineering VA United States Marwadi University Faculty of Management Studies India Pune Technological University Computer Science Engineering College of Engineering Maharashtra Pune India Ajman University College of Mass Communication Ajman United Arab Emirates

ISBN: (纸本)9798331508685

Numerous diverse learning materials can be found on e-learning sites. Students in today's e-learning platforms invest a lot of time and energy in locating pertinent learning materials. The student's actual requirements must be taken into account based on a variety of characteristics, including choices, expertise, and learning style. Education must be pertinent to the necessary concept's environment. This study aims to develop an efficient approach for detecting e-learning style and then customizing the e-learning contents to match that style, using machine learning (ML) algorithms to enhance personalization in e-learning. The blended ensemble method combined with the XGBoost meta-learning approach produced the most prominent results for enhancing e-learning style, with an accuracy of 97.6%. Further, the textual material of the e-files is altered using various natural language processing (NLP) approaches. The spaCy NLP-oriented labeled entity identification (LEI) algorithm achieves a 94.2% F1 value and a 0.92 precise match ratio while color-coded textual production of 10 e-files with 790 different phrases. These alterations are intended to suit students' tastes, resulting in an additional personalized and interactive teaching encounter. © 2025 IEEE.

关键词： and natural language processing Artificial intelligence E-learning Machine learning Students

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Sibyl: Empowering Empathetic Dialogue Generation in Large language Models via Sensible and Visionary Commonsense Inference 31

Sibyl: Empowering Empathetic Dialogue Generation in Large La...

引用

31st international conference on Computational Linguistics, COLING 2025

作者： Wang, Lanrui Li, Jiangnan Yang, Chenxu Lin, Zheng Tang, Hongyin Liu, Huan Cao, Yanan Wang, Jingang Wang, Weiping Institute of Information Engineering Chinese Academy of Sciences Beijing China School of Cyber Security University of Chinese Academy of Sciences Beijing China WeChat AI Tencent Inc. China Meituan Group Beijing China

ISBN: (纸本)9798891761964

Recently, there has been a heightened interest in building chatbots based on Large language Models (LLMs) to emulate human-like qualities in multi-turn conversations. Despite having access to commonsense knowledge to better understand the psychological aspects and causality of dialogue context, even these powerful LLMs struggle to achieve the goals of empathy and emotional support. Current commonsense knowledge derived from dialogue contexts is inherently limited and often fails to adequately anticipate the future course of a dialogue. This lack of foresight can mislead LLMs and hinder their ability to provide effective support. In response to this challenge, we present an innovative framework named Sensible and Visionary Commonsense knowledge (Sibyl). Designed to concentrate on the immediately succeeding dialogue, this paradigm equips LLMs with the capability to uncover the implicit requirements of the conversation, aiming to elicit more empathetic responses. Experimental results demonstrate that incorporating our paradigm for acquiring commonsense knowledge into LLMs comprehensively enhances the quality of their responses. © 2025 Association for Computational Linguistics.

关键词： Computational linguistics

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A Review and Comparison of Competency Question engineering Approaches 24th

A Review and Comparison of Competency Question Engineering...

引用

24th international conference on knowledge engineering and knowledge Management, EKAW 2024

作者： Alharbi, Reham Tamma, Valentina Grasso, Floriana Payne, Terry R. University of Liverpool Liverpool United Kingdom Taibah University Madinah Saudi Arabia

ISBN: (纸本)9783031777912

Competency Questions (CQs) are essential in ontology engineering;they express an ontology’s functional requirements through natural language questions, offer crucial insights into an ontology’s scope, and are pivotal for various tasks, such as ontology reuse, testing, requirement specification, and pattern definition. CQ engineering approaches are gaining prominence, transitioning from manual to automatic methods, each with its own purpose, techniques, outcomes, and limitations. In this paper, we provide a review aimed at positioning these approaches within a formal categorisation, highlighting the main challenges among them and facilitating the inclusion of upcoming initiatives, and propose a benchmark to support comparative studies. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Requirements engineering

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international Workshop on Trustworthy Federated Learning, FL 2024

International Workshop on Trustworthy Federated Learning, FL...

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international Workshop on Trustworthy Federated Learning, FL 2024

ISBN: (纸本)9783031822391

The proceedings contain 12 papers. The special focus in this conference is on Trustworthy Federated Learning. The topics include: Supercharging Federated Learning with Flower and NVIDIA FLARE;federated knowledge Transfer Fine-Tuning Large Server Model with Resource-Constrained IoT Clients;federated Incomplete Multi-view Clustering with Heterogeneous Graph Neural Networks;benchmarking Data Heterogeneity Evaluation Approaches for Personalized Federated Learning;heterogeneous Federated Learning with Convolutional and Spiking Neural Networks;synthetic Data Aided Federated Learning Using Foundation Models;leveraging Unstructured Text Data for Federated Instruction Tuning of Large language Models;fedLegal: A Real-World Federated Learning Benchmark for Legal natural language processing;lightweight Unsupervised Federated Learning with Pretrained Vision language Model;enhancing Causal Discovery in Federated Settings with Limited Local Samples.

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A Review of Chatbot Technology: Its Design and Implementation

A Review of Chatbot Technology: Its Design and Implementatio...

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international conference on Power engineering and Intelligent Systems, PEIS 2024

作者： Rawat, Sneha Kumari, Ritika Bansal, Poonam Department of Artificial Intelligence and Data Sciences IGDTUW Delhi India USICT GGISPU Delhi India

ISBN: (纸本)9789819767090

Chatbot technology has experienced rapid growth in recent times, and these intelligent systems engage in real-time conversations with humans using natural language. Chatbots act as AI-powered entities which can simultaneously engage with numerous individuals, capable of providing instant responses. Virtual assistants are the common name for such programs. This paper aims to discuss chatbots’ classification, focusing on its design and implementation, also analyzing the techniques and models used for its development, and its utilization across diverse industries along the limitations of this technology by doing the comprehensive review and analysis of research papers published in various research journals through the years 2016 to 2020. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Artificial intelligence Chatbot natural language processing Text feature extraction

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Performance Evaluation of Reddit Comments Using Machine Learning and natural language processing Methods in Sentiment Analysis 30th

Performance Evaluation of Reddit Comments Using Machine Lear...

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30th international conference on Computational and Experimental engineering and Sciences

作者： Zhang, Xiaoxia Qi, Xiuyuan Teng, Zixin Shanghaitech Univ Shanghai Peoples R China

ISBN: (纸本)9783031774911;9783031774898;9783031774881

Sentiment analysis, an increasingly vital field in both academia and industry, plays a pivotal role in machine learning applications, particularly on social media platforms like Reddit. However, the efficacy of sentiment analysis models is hindered by the lack of expansive and fine-grained emotion datasets. To address this gap, our study leverages the GoEmotions dataset, comprising a diverse range of emotions, to evaluate sentiment analysismethods across a substantial corpus of 58,000 comments. Distinguished from prior studies by the Google team, which limited their analysis to only two models, our research expands the scope by evaluating a diverse array of models. We investigate the performance of traditional classifiers such as Naive Bayes and Support Vector Machines (SVM), as well as state-of-the-art transformer-based models including BERT, RoBERTa, and GPT. Furthermore, our evaluation criteria extend beyond accuracy to encompass nuanced assessments, including hierarchical classification based on varying levels of granularity in emotion categorization. Additionally, considerations such as computational efficiency are incorporated to provide a comprehensive evaluation framework. Our findings reveal that the RoBERTa model consistently outperforms the baseline models, demonstrating superior accuracy in fine-grained sentiment classification tasks. This underscores the substantial potential and significance of the RoBERTa model in advancing sentiment analysis capabilities.

关键词： Sentiment Analysis Machine Learning natural language processing Reddit RoBERTa

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A Novel Track Initiation Method based on Rule knowledge and Deep Detection Network 16

A Novel Track Initiation Method based on Rule Knowledge and ...

引用

16th international conference on Signal processing Systems, ICSPS 2024

作者： Wang, Zengke Zhang, Weijie Pan, Meiyan Liu, Dai Xi'an Electronic Engineering Research Institute China

ISBN: (纸本)9781510689251

Radar target tracking plays an important role in reconnaissance, monitoring and defense. Clutter caused by complex electronic and natural environments poses many challenges to radar target tracking. The current track initiation method, which heavily relies on empirical knowledge, struggles with the increasing problem of false tracks and missed detections. To address this, a track initiation method based on rule knowledge and a deep detection network is proposed. In this method, radar echoes are converted into images that comprehensively reflect the different frames and target shapes, facilitating processing. With the help of the excellent detection, positioning and recognition ability of the deep learning network Faster-RCNN, the candidate boxes where might exist target track in the echo image are extracted candidate boxes where targets might exist in the echo image are extracted. Additionally, the corresponding angle and wave gate are set as initiation rules. The combination of radar data learning and partial experiential rules reduces the need for accurate prior parameters in traditional methods. Furthermore, by considering the spatial and temporal information of the target comprehensively, it achieves a good balance between the correct initiation rate and false initiation rate. Simulation environments under different observation trajectories are constructed, and Monte Carlo simulation is used to compare the proposed method with the traditional track initiation method and other track initiation methods combined with machine learning and prior rules. Results show that our method can ensure the correct initiation rate of the target track and effectively suppress the false initiation rate. © 2025 SPIE.

关键词： Deep learning

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A Framework Based on Data Augmentation for knowledge Graph Entity Typing

A Framework Based on Data Augmentation for Knowledge Graph E...

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2025 IEEE international conference on Acoustics, Speech, and Signal processing, ICASSP 2025

作者： Li, Zepeng Zhai, Minyu Li, Xiao Huang, Rikui Liang, Chenhui Hu, Bin School of Information Science & Engineering Lanzhou University Lanzhou China School of Computer Science & Technology Huazhong University of Science and Technology Wuhan China

ISBN: (纸本)9798350368741

The task of knowledge graph entity typing (KGET) aims to infer the missing types for entities in knowledge graphs, which is a significant subtask of knowledge graph completion (KGC). In despite of its progress, we observe that the sparsity of the dataset greatly affects the task itself as well as downstream tasks. In this paper, we propose a framework to alleviate this problem, which consists of data augmentation and type inference. We introduce a Statistics-based Entity Type Data Augmentation (SET-DA) method in data augmentation phase, which calculates a type probability distribution for each entity by statistically determining the global relation-type statistics information, and then employ embedding-based KGET models for type inference. Experimental results on two widely used datasets indicate that the proposed framework can solve the data sparsity problem in the task of KGET and the performance of models trained with data augmentation by SET-DA significantly outperforms previous state-of-the-art methods. © 2025 IEEE.

关键词： Data Augmentation knowledge Graph Completion knowledge Graph Entity Typing

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