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

共 26995 条记录，以下是41-50 订阅

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13th CCF international conference on natural language processing and Chinese Computing, NLPCC 2024

13th CCF International Conference on Natural Language Proces...

引用

13th CCF international conference on natural language processing and Chinese Computing, NLPCC 2024

ISBN: (纸本)9789819794300

The proceedings contain 194 papers. The special focus in this conference is on natural language processing and Chinese Computing. The topics include: Hierarchical knowledge Aggregation for Personalized Response Generation in Dialogue Systems;multi-hop Reading Comprehension Model Based on Abstract Meaning Representation and Multi-task Joint Learning;Leveraging Large language Models for QA Dialogue Dataset Construction and Analysis in Public Services;MCFC: A Momentum-Driven Clicked Feature Compressed Pre-trained language Model for Information Retrieval;integrating Syntax Tree and Graph Neural Network for Conversational Question Answering over Heterogeneous Sources;pqE: Zero-Shot Document Expansion for Dense Retrieval with Large language Models;CKF: Conditional knowledge Fusion Method for CommonSense Question Answering;MPPQA: Structure-Aware Extractive Multi-span Question Answering for Procedural Documents;GraphLLM: A General Framework for Multi-hop Question Answering over knowledge Graphs Using Large language Models;local or Global Optimization for Dialogue Discourse Parsing;structure and Behavior Dual-Graph Reasoning with Integrated Key-Clue Parsing for Multi-party Dialogue Reading Comprehension;enhancing Emotional Support Conversation with Cognitive Chain-of-Thought Reasoning;a Simple and Effective Span Interaction Modeling Method for Enhancing Multiple Span Question Answering;FacGPT: An Effective and Efficient Method for Evaluating knowledge-Based Visual Question Answering;PAPER: A Persona-Aware Chain-of-Thought Learning Framework for Personalized Dialogue Response Generation;towards Building a Robust knowledge Intensive Question Answering Model with Large language Models;model-Agnostic knowledge Distillation Between Heterogeneous Models;exploring Multimodal Information Fusion in Spoken Off-Topic Degree Assessment;integrating Hierarchical Key Information and Semantic Difference Features for Long Text Matching;CausalAPM: Generalizable Literal Disentanglement for NLU

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13th CCF international conference on natural language processing and Chinese Computing, NLPCC 2024

13th CCF International Conference on Natural Language Proces...

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13th CCF international conference on natural language processing and Chinese Computing, NLPCC 2024

ISBN: (纸本)9789819794393

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

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

引用

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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Research on Named Entity Recognition in Ancient Chinese Based on Incremental Pre-training and Domain Lexicon 13th

Research on Named Entity Recognition in Ancient Chinese Base...

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13th international conference on natural language processing and Chinese Computing

作者： Kang, Wenjun Zuo, Jiali Dai, Qili Hu, Yiyu Wang, Mingwen Jiangxi Normal Univ Sch Comp & Informat Engn Nanchang Jiangxi Peoples R China Nanchang Inst Technol Sch Comp Informat Engn Nanchang Jiangxi Peoples R China Guangzhou Inst Sci & Technol Sch Comp Sci & Engn Guangzhou Peoples R China

ISBN: (纸本)9789819794300;9789819794317

Currently, there is limited research on ancient Chinese named entity recognition, primarily due to the scarcity of publicly available datasets for model training. We constructed a CMAG-NER dataset based on the "Comprehensive Mirror for Aid in Government". Addressing the challenges faced by existing models in identifying person entities with omitted surnames and determining entity boundaries in ancient Chinese texts, we integrated the LEBERT-CRF model with a general domain lexicon and the "Comprehensive Mirror for Aid in Government Dictionary" to fuse external statistical information and rule-based knowledge, thereby enhancing the performance of ancient Chinese named entity recognition. Additionally, to improve the model's comprehension of ancient Chinese, we have compiled a substantial corpus of ancient Chinese literature for incremental pre-training of BERT-Ancient-Chinese. Experimental results demonstrate that the proposed method effectively mitigates the aforementioned challenges, but the performance of large models on this task still requires improvement.

关键词： Chinese Named Entity Recognition Data Annotation Domain knowledge Large language Models

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Towards Building a Robust knowledge Intensive Question Answering Model with Large language Models 13th

Towards Building a Robust Knowledge Intensive Question Answe...

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13th international conference on natural language processing and Chinese Computing

作者： Hong, Xingyun Shao, Yan Wang, Zhilin Duan, Manni Jin, Xiongnan Zhejiang Lab Hangzhou Peoples R China Hangzhou Res & Dev Ctr China Mobile Hangzhou Peoples R China

ISBN: (纸本)9789819794300;9789819794317

The development of LLMs has greatly enhanced the intelligence and fluency of question answering, while the emergence of retrieval enhancement has enabled models to better utilize external information. However, the presence of noise and errors in retrieved information poses challenges to the robustness of LLMs. In this work, to evaluate the model's performance under multiple interferences, we first construct a dataset based on machine reading comprehension datasets simulating various scenarios, including critical information absence, noise, and conflicts. To address the issue of model accuracy decline caused by noisy external information, we propose a data augmentation-based fine-tuning method to enhance LLM's robustness against noise. Additionally, contrastive learning approach is utilized to preserve the model's discrimination capability of external information. We have conducted experiments on both existing LLMs and our approach, the results are evaluated by GPT-4, which indicates that our proposed methods improve model robustness while strengthening the model's discrimination capability.

关键词： Retrieval-augmented LLM Question answering Robustness

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Reparameterization-Based Parameter-Efficient Fine-Tuning Methods for Large language Models: A Systematic Survey 13th

Reparameterization-Based Parameter-Efficient Fine-Tuning Met...

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13th international conference on natural language processing and Chinese Computing

作者： Chen, Zezhou Liu, Zhaoxiang Wang, Kai Lian, Shiguo China Unicom Ai Innovat Ctr Beijing 100013 Peoples R China China Unicom Unicom Digital Technol Beijing 100013 Peoples R China

ISBN: (纸本)9789819794362;9789819794379

The rapid advancement of Large language Models (LLMs) has revolutionized both academia and industry, leveraging Transformer architectures and pre-training objectives to achieve unprecedented performance. To fully exploit the potential of LLMs, fine-tuning LLMs on specific downstream tasks is essential. However, traditional full fine-tuning methods pose significant computational challenges, prompting the emergence of Parameter-Efficient Fine-Tuning (PEFT) methods, especially reparameterization-based PEFT methods. In this survey, we delve into reparameterization-based PEFT methods, which aim to fine-tune LLMs with reduced computational costs while preserving their knowledge. We systematically analyze their design principles and divide these methods into six categories. We analyze the training parameter complexity, GPU memory consumption, training time costs, accuracy and limitations of each method. We summarize challenges within the reparameterization-based PEFT methods and propose future directions.

关键词： Large language Models Parameter-Efficient Fine-Tuning Reparameterization

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Advancing Robotics Education: Integrating Large language Models for natural language Programming in VET 25th

Advancing Robotics Education: Integrating Large Language Mod...

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25th international conference on Intelligent Data engineering and Automated Learning

作者： Prieto, Abraham Romero, Alejandro Bellas, Francisco Univ A Coruna Integrated Grp Engn Res La Coruna Spain

ISBN: (纸本)9783031777370;9783031777387

This paper presents an educational activity developed within the AIM@VET project, aimed at integrating Large language Models (LLMs) into Vocational Education and Training (VET) for programming robots using natural language. The curriculum covers key AI topics such as Human-Robot Interaction (HRI), natural language processing, and the use of advanced models like ChatGPT. Students engage in activities from basic command interpretation to advanced voice-controlled interactions, gaining practical experience with LLMs in robotics. Evaluations showed significant improvements in understanding and engagement, highlighting the effectiveness of LLMs in enhancing robotics education for VET students.

关键词： Large language Models Vocational Education Robotics Human-Robot Interaction natural language Programming

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Improving Self-consistency for Open-Domain Question Answering via Automatic Prompt engineering and Ensemble Learning 13th

Improving Self-consistency for Open-Domain Question Answerin...

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13th international conference on natural language processing and Chinese Computing

作者： Li, Jie Han, Xue Deng, Chao Feng, Junlan China Mobile Res Inst JiuTian Team Beijing Peoples R China

ISBN: (纸本)9789819794331;9789819794348

Large language Models (LLMs) have shown remarkable performance on a variety of natural language tasks, but eliciting their abilities for more consistent predictions is still highly reliant on researchers' well-crafted prompts, which often require costly computational consumption as well as onerous trial-and-error efforts. For generative tasks such as open-domain question answering (QA), LLMs face a particularly great challenge. To alleviate the issue in QA tasks, we propose a scheme that can both automatically optimize and efficiently leverage candidate prompts to obtain consistent and accurate predicted answers. Superior self-consistency of LLMs' predictions requires diverse high-quality reasoning paths, thus multiple candidate prompts in the iterative optimization process of automatic prompt engineering (APE) are fully leveraged to drive LLMs to generate multiple reasoning paths leading to improved self-consistency. The evaluation performance of the candidate prompt on the sampled dataset is used as a reference (similar to the weights of the base learners in ensemble learning) to determine its contribution to the final answer. Experimental results demonstrate that our scheme yields more reliable and diverse predicted answers and outperforms conventional self-consistency baseline models in several typical QA benchmark tests.

关键词： Question answering Self-consistency Prompt learning Ensemble learning

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Literature Hunter: Literature Reading Aided by Large language Models 13th

Literature Hunter: Literature Reading Aided by Large Languag...

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13th international conference on natural language processing and Chinese Computing

作者： Lai, Yahao Chen, Xiang Du, Yunchen Liu, Bo Zhao, Shaofeng Beijing Informat Sci & Technol Univ Beijing 100192 BJ Peoples R China Zhengzhou Univ Zhengzhou 450001 HN Peoples R China Henan Univ Econ & Law Zhengzhou 450001 HN Peoples R China

ISBN: (纸本)9789819794423;9789819794430

Literature reading is an indispensable process for understanding a research field, but it often involves a significant investment of time. To enable researchers to quickly understand the advancements in a particular research field, we leverage the writing capabilities of large language models (LLMs) and propose an end-to-end survey paper writing system that can complete a paper using only a few keywords. Specifically, we closely mimic the human writing process by (1) generating an outline from multiple perspectives, (2) selecting the best one from the generated candidate content, and (3) refining the selected content. Furthermore, to evaluate the generated content, we curate SurGen, a dataset of recent high-quality survey articles, and conduct tests. Experimental results demonstrate that our method significantly improves both automated metrics and human evaluations compared to direct generation. To the best of our knowledge, we are the first to attempt generating survey papers using large language models and to release the corresponding dataset.

关键词： Large language models Literature generation Chain of thought

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CUFE@NLU of Devanagari Script languages 2025: language Identification using fastText 1

CUFE@NLU of Devanagari Script Languages 2025: Language Ident...

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1st Workshop on Challenges in processing South Asian languages, CHiPSAL 2025, co-located with the 31st international conference on Computational Linguistics, COLING 2025

作者： Ibrahim, Michael Computer Engineering Department Cairo University 1 Gamaa Street Giza12613 Egypt

ISBN: (纸本)9798891762015

language identification is a critical area of research within natural language processing (NLP), particularly in multilingual contexts where accurate language detection can enhance the performance of various applications, such as machine translation, content moderation, and user interaction systems. This paper presents a language identification system developed using fastText. In the CHIPSAL@COLING 2025 Task on Devanagari Script language Identification, the proposed method achieved first place, with an F1 score of 0.9997. © 2025 international Committee on Computational Linguistics.

关键词： Computational linguistics

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