{"product_id":"natural-language-processing-and-chinese-computing-9789819533428","title":"Natural Language Processing and Chinese Computing","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cp\u003e\u003cstrong\u003e.- Information Extraction and Knowledge Graph.\u003c\/strong\u003e\u003cbr\u003e.- Progressive Training of Transformer for Knowledge Graph Completion Tasks.\u003cbr\u003e.- Document-level Event Coreference Resolution on Trigger Augmentation and Contrastive Learning.\u003cbr\u003e.- Dynamic Chain-of-thought for Low-Resource Event Extraction.\u003cbr\u003e.- On Sentence-level Non-adversarial Robustness of Chinese Named Entity Recognition with Large Language Models.\u003cbr\u003e.- Spatial Relation Classification on Supervised In-Context Learning.\u003cbr\u003e.- HGNN2KAN: Distilling hypergraph neural networks into KAN for efficient inference.\u003cbr\u003e.- Adapting Task-General ORE Systems for Extracting Open Relations between Fictional Characters in Chinese Novels.\u003cbr\u003e.- DRLF:Denoiser-Reinforcement Learning Framework for Entity Completion.\u003cbr\u003e.- Fashion-related Attribute Value Extraction with Visual Prompting.\u003cbr\u003e.- Discovering Latent Relationship for Temporal Knowledge Graph Reasoning.\u003cbr\u003e.- Logical Rule-Constrained Large Language Models for Document-Level Relation Extraction.\u003cbr\u003e.- An Adaptive Semantic-Aware Fusion Method for Multimodal Entity Linking.\u003cbr\u003e.- Retrieve, Interaction, Fusion: a Simple Approach in Ancient Chinese Named Entity Recognition.\u003cbr\u003e.- Reasoning-Guided Prompt Learning with Historical Knowledge Injection for Ancient Chinese Relation Extraction.\u003cbr\u003e.- MMD-TKGR: Multi-Agent Multi-Round Debate for Temporal Knowledge Graph Reasoning.\u003cbr\u003e.- AutoPRE: Discovering Concept Prerequisites with LLM Agents.\u003cbr\u003e.- Weakly-Supervised Generative Framework for Product Attribute Identification in Live-Streaming E-Commerce.\u003cbr\u003e.- Exploring Representation-Efficient Transfer Learning Approaches for Speech Recognition and Translation Using Pre-trained Speech Models.\u003cbr\u003e.- A Neighborhood Aggregation-based Knowledge Graph Reasoning Approach in Operations and Maintenance.\u003cbr\u003e.- CARE: Contextual Augmentation with Retrieval Enhancement for Relation Extraction in Large Language Models.\u003cbr\u003e.- RHDG: Retrieval-augmented Heuristics-driven Demonstration Generation for Document-Level Event Argument Extraction.\u003cbr\u003e\u003cstrong\u003e.- Large Language Models and Agents.\u003c\/strong\u003e\u003cbr\u003e.- Beyond One-Size-Fits-All: Adaptive Fine-Tuning for LLMs Based on Data Inherent Heterogeneity.\u003cbr\u003e.- From Chain to Loop: Improving Reasoning Capability in Small Language Models via Loop-of-Thought.\u003cbr\u003e.- TaxBen: Benchmarking the Chinese Tax Knowledge of Large Language Models.\u003cbr\u003e.- Propagandistic Meme Detection via Large Language Model Distillation.\u003cbr\u003e.- Multi-Candidate Speculative Decoding.\u003cbr\u003e.- Debate-Driven Legal Reasoning: Disambiguating Confusing Charges through Multi-Agent Debate.\u003cbr\u003e.- A Human-Centered AI Agent Framework with Large Language Models for Academic Research Tasks.\u003cbr\u003e.- ReGA: Reasoning and Grounding Decoupled GUI Navigation Agents.\u003cbr\u003e.- PSYCHE: Practical Synthetic Math Data Evolution. \u003cbr\u003e.- MultiJustice: A Chinese Dataset for Multi-Party, Multi-Charge Legal Prediction.\u003cbr\u003e.- Reward-Guided Many-Shot Jailbreaking.\u003cbr\u003e.- Self-Prompt Tuning: Enable Autonomous Role-Playing in LLMs.\u003cbr\u003e.- RASR: A Multi-Perspective RAG-based Strategy for Semantic Textual Similarity.\u003cbr\u003e.- H2HTALK: Evaluating Large Language Models as Emotional Companion.\u003cbr\u003e.- EvoP: Robust LLM Inference via Evolutionary Pruning.\u003cbr\u003e.- Large Language Model based Multi-Agent Learning for Mixed Cooperative-Competitive Environments.\u003cbr\u003e.- EduMate:LLM-Powered Detection of Student Learning Emotions and Efficacy in Semi-Structured Counseling.\u003cbr\u003e.- MAD-HD: Multi-Agent Debate-Driven Ungrounded Hallucination Detection.\u003cbr\u003e.- TIANWEN: A Comprehensive Benchmark for Evaluating LLMs in Chinese Classical Poetry Understanding and Reasoning.\u003cbr\u003e.- RKE-Coder: A LLMs-based Code Generation Framework with Algorithmic and Code Knowledge Integration.\u003cbr\u003e.- See Better, Say Better: Vision-Augmented Decoding for Mitigating Hallucinations in Large Vision-Language Models.\u003cbr\u003e.- Exploring Large Language Models for Grammar Error Explanation and Correction in Indonesian as a Low-Resource Language.\u003cbr\u003e.- Libra: Large Chinese-based Safeguard for AI Content.\u003cbr\u003e.- FADERec: Fine-grained Attribute Distillation Enhanced by Collaborative Fusion for LLM-based Recommendation.\u003cbr\u003e.- Improving RL Exploration for LLM Reasoning through Retrospective Replay.\u003c\/p\u003e","brand":"Springer","offers":[{"title":"Default Title","offer_id":53212856189271,"sku":"9789819533428","price":80.74,"currency_code":"GBP","in_stock":true}],"url":"https:\/\/bookcurl.com\/products\/natural-language-processing-and-chinese-computing-9789819533428","provider":"Book Curl","version":"1.0","type":"link"}