Natural Language Processing and Chinese Computing

13th National CCF Conference, NLPCC 2024, Hangzhou, China, November 1–3, 2024, Proceedings, Part I

AvDerek F. Wong,Zhongyu Wei

Häftad, Engelska, 2024

Del 15359 i serien Lecture Notes in Computer Science

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Beskrivning

The five-volume set LNCS 15359 - 15363 constitutes the refereed proceedings of the 13th National CCF Conference on Natural Language Processing and Chinese Computing, NLPCC 2024, held in Hangzhou, China, during November 2024.The 161 full papers and 33 evaluation workshop papers included in these proceedings were carefully reviewed and selected from 451 submissions. They deal with the following areas: Fundamentals of NLP; Information Extraction and Knowledge Graph; Information Retrieval, Dialogue Systems, and Question Answering; Large Language Models and Agents; Machine Learning for NLP; Machine Translation and Multilinguality; Multi-modality and Explainability; NLP Applications and Text Mining; Sentiment Analysis, Argumentation Mining, and Social Media; Summarization and Generation.

Produktinformation

Utgivningsdatum:2024-11-04
Mått:155 x 235 x 30 mm
Vikt:832 g
Format:Häftad
Språk:Engelska
Serie:Lecture Notes in Computer Science
Antal sidor:519
Förlag:Springer Verlag, Singapore
ISBN:9789819794300

Utforska kategorier

Systemvetenskap och AI inom Data och IT

Innehållsförteckning

Overcoming Rigid and Monotonous: Enhancing Knowledge-grounded Conversation Generation via Multi-granularity Knowledge.- Learning to Generate Style-Specific Adapters for Stylized Dialogue Generation.- 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 Debiasing.- W2CL:A Multi-task Learning Approach to Improve Domain-Specific Sentence Classification through Word Classification and Contrastive Learning.- Outperforming Larger Models on Text Classification Through Continued Pre-Training.- Semantic Knowledge Enhanced and Global Pointer Optimized Method for Medical Nested Entity Recognition.- CSLAN: A Novel Lexicon Attention Network for Chinese NERS2D: Enhancing Zero-shot Cross-lingual Event Argument Extraction with Semantic Knowledge.- Bias-Rectified Multi-way Learning with Data Augmentation for Implicit Discourse Relation Recognition.- Retrieval-enhanced Template Generation for Template Extraction.- Chinese Named Entity Recognition Based on Template and Contrastive Learning.- Enhancing Logical Rules Based on Self-Distillation for Document-Level Relation ExtractionPrompt-based Joint Contrastive Learning for Zero-Shot Relation ExtractionLow-Resource Event Causality Identification With Global Consistency Constraints.- Only One Relation Possible? Modeling the Ambiguity in Temporal Relation Extraction.- Empowering LLMs for Long-text Information Extraction in Chinese Legal Documents.- LLMADR: A Novel Method for Adverse Drug Reaction Extraction Based on Style Aligned Large Language Models Fine-tuning.- Research on Named Entity Recognition in Ancient Chinese Based on Incremental Pre-training and Domain Lexicon.- MCKRL: A Multi-Channel based Multi-Graph Knowledge Representation Learning Model.