{"product_id":"natural-language-processing-and-chinese-computing-9789819533480","title":"Natural Language Processing and Chinese Computing","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cp\u003e.- \u003cstrong\u003eIR \/ Dialogue Systems \/ Question Answering\u003cbr\u003e\u003c\/strong\u003e.- TriG-RAG: Triple-Granularity Fusion for Retrieval-Augmented Generation with Adaptive Context-Relation Balance.\u003cbr\u003e.- Knowledge Distillation for Large Language Models Based on Global Keywords and Chain of Thought.\u003cbr\u003e.- Exploring the Capabilities of Chinese LLMs in the Legal Field.\u003cbr\u003e.- RoleplayLLM: Enhancing Role-Playing Abilities in Large Language Models via Multidimensional Fine Tuning and Preference Optimization.\u003cbr\u003e.- Multi-task Contrastive Learning Enhanced Instruction Tuning for Dialog Understanding.\u003cbr\u003e.- DynaRAG: Adaptive Context Compression via Reinforcement Learning for Enhanced Retrieval-Augmented Generation.\u003cbr\u003e.- Retrieving and Reading Multimodal Documents for Knowledge-based VQA.\u003cbr\u003e.- Relation-Aware Graph Reasoning for Multiple Choice Question Answering.\u003cbr\u003e.- Enhancing Depression-Diagnosis-Oriented Chat with Psychological State Tracking. \u003cbr\u003e.- ReD-RAG: Rewritable Decomposition-Guided Iterative Retrieval Augmented Generation for Open Domain Multi-Hop Reasoning.\u003cbr\u003e.- Bridging Questions and Charts: A Weakly Supervised Alignment Model for Chart Question Answering. \u003cbr\u003e.- FRAG: Focused Retrieval Augmented Generation Reducing Retrieval Scope by Mapping Table.\u003cbr\u003e.- Ensuring Context Completeness in Retrieval-Augmented Generation for Knowledge-Intensive Question-answering.\u003cbr\u003e.- \u003cstrong\u003eMachine Translation and Multilinguality\u003cbr\u003e\u003c\/strong\u003e.- Improving LLM-based Document-level MT with Multi-Knowledge Fusion.\u003cbr\u003e.- Continual Learning for Multilingual Neural Machine Translation via Meta-Contrastive Memory Replay.\u003cbr\u003e.- Prompting and Consistency Learning Strategies for Multimodal Grammatical Error Correction in Low Error Density Domains.\u003cbr\u003e.- Bias Beyond English: Evaluating Social Bias and Debiasing Methods in a Low-Resource Setting.\u003cbr\u003e.- Doc-Guided Sent2Sent++: A Sent2Sent++ Agent with Doc-Guided memory for Document-level Machine Translation.\u003cbr\u003e.- Fine-Grained Contrastive Learning for End-to-End Vietnamese Text Image Machine Translation.\u003cbr\u003e.- Adaptive Inner Speech Text Alignment for LLM-based Speech Translation.\u003cbr\u003e.- Fine-Tuning for Low-Resource Language Machine Translation Using Large Language Models Integrated with Dependency Parsing Rule.\u003cbr\u003e.- Simplify Translate: A Unified Framework for Accessible Machine Translation.\u003cbr\u003e.- PGATA: Phonology-and-Glyph-Aware Token Alignment for Transfer Learning in Cantonese Sarcasm Detection.\u003cbr\u003e.- LLM-enhanced Translation for Low-resource Languages: Cross-lingual Alignment and Multi-domain Adaptation.\u003cbr\u003e.- \u003cstrong\u003eSentiment analysis \/ Argumentation Mining \/ Social Media\u003cbr\u003e\u003c\/strong\u003e.- Best-Fit Document: Enhancing Compositional Generalization in Multi-label Text Classification.\u003cbr\u003e.- Dialogue Multi-dimensional Feature Dividing and Fusion Model for Dialogue Aspect-based Sentiment Quadruple Analysis.\u003cbr\u003e.- Boosting Affective Events Classification: A Contextual Framework with Chain-of-Thought Prompt.\u003cbr\u003e.- Hate Speech Detection in Somali-English Code-Switched Texts.\u003cbr\u003e.- Chinese Spelling Correction for Social-Platform Internet Slang Texts.\u003cbr\u003e.- DiGTF: A Difference-Guided Two-Stage Fusion Framework for Multimodal Sentiment Analysis. \u003cbr\u003e.- EVL-MCoT: Enhanced Vision-Language Multi-CoT for Harmful Meme Detection.\u003cbr\u003e.- Deep Graph Neural Point Process For Learning Temporal Interactive Networks.\u003cbr\u003e.- Bayesian Network-based Adaptive Prompt Learning for Emotion-Cause Pair Extraction.\u003cbr\u003e.- Exploring Sentiment Analysis in Tigrigna: Insights from Social Media Texts.\u003cbr\u003e.- Unlocking the Advantage of Context Interaction via Bi-Graph Reasoning for Document-level Aspect-based Sentiment Analysis.\u003cbr\u003e.- EMAO: Expectation-Maximization and Adaptive Objective for Microscopic Cascade Prediction.\u003cbr\u003e.- Attributed Graph Clustering with Dual Contrastive Regularization.\u003cbr\u003e.- Deep Learning-Based Knowledge Injection for Metaphor Detection: A Comprehensive Review.\u003cbr\u003e.- CADA: A Counterfactual Adversarial Data Augmentation Framework for Low-Resource Hate Speech Detection.\u003cbr\u003e.- Semantic Information Enhanced Fake News Detection.\u003cbr\u003e.- Fine-grained Inappropriate Speech Detection Based on Momentum Contrastive Learning.\u003cbr\u003e.- ISIPN: Intention-Semantic Incongruity Perception Network for Multimodal Metaphor Detection.\u003cbr\u003e.- Self-Distillation Across Modalities: Enhancing Cross-Modal Correlation Perception for Multimodal Fake News Detection.\u003cbr\u003e.- D-PathVER: Dynamic Reasoning Paths for Complex Claim Verification.\u003cbr\u003e.- Dynamic Multi-Views In-Context Learning with Large Language Models for Aspect-Based Sentiment Analysis.\u003cbr\u003e.- MAGI: Modality-Aligned Geometry-aware Integration for Robust Multimodal Sentiment Analysis.\u003c\/p\u003e","brand":"Springer","offers":[{"title":"Default Title","offer_id":53212856156503,"sku":"9789819533480","price":80.74,"currency_code":"GBP","in_stock":true}],"url":"https:\/\/bookcurl.com\/products\/natural-language-processing-and-chinese-computing-9789819533480","provider":"Book Curl","version":"1.0","type":"link"}