Natural Language Processing and Information Systems (eBook, PDF)

30th International Conference on Applications of Natural Language to Information Systems, NLDB 2025, Kanazawa, Japan, July 4-6, 2025, Proceedings, Part I
Redaktion: Ichise, Ryutaro

• Format: PDF

Produktbeschreibung

The two-volume set LNCS 15836 and 15837 constitutes the proceedings of the 30th International Conference on Applications of Natural Language to Information Systems, NLDB 2025, held in Kanazawa, Japan, during July 4-6, 2025. The 33 full papers, 19 short papers and 2 demo papers presented in this volume were carefully reviewed and selected from 120 submissions. The proceedings contain novel and significant research contributions addressing theoretical aspects, algorithms, applications, architectures, resources, and other aspects of NLP, as well as survey and discussion papers.

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Produktdetails

• Verlag: Springer Nature Switzerland
• Seitenzahl: 503
• Erscheinungstermin: 30. Juni 2025
• Englisch
• ISBN-13: 9783031971419
• Artikelnr.: 74769900

Inhaltsangabe

.- Full papers (Main Track).

.- Multi-Scale Convolutional Fusion with Contrastive Feature Alignment for Imbalanced Data Classification.

.- Memory Efficient LM Compression using Fisher Information from Low-Rank Representations.

.- Accelerating End-to-End PDF to Markdown Conversion through Assisted Generation.

.- Comparison of pipelines, seq2seq models, and LLMs for rare disease information extraction.

.- Multiple Graphical Convolution Networks Approach for Aspect-Based Sentiment Classification.

.- How important is domain-specific language model pretraining and instruction finetuning for biomedical relation extraction?.

.- ShortPathQA: A Dataset for Controllable Fusion of Large Language Models with Knowledge Graphs.

.- Explaining Bias in Internal Representations of Large Language Models via Concept Activation Vectors.

.- Duplicate Bug Report Retrieval for New Bug Reports.

.- Context Is The Key For LLM-Based Text Segmentation.

.- MultiProSE: A Multi-label Arabic Dataset for Propaganda, Sentiment, and Emotion Detection.

.- CLIP-Driven Deep Hashing for Cross-Modal Retrieval.

.- WIP: Iterative Post-Training Pruning with Weighted Importance Estimation for Large Language Models.

.- A Comparative Study on the Development of a Thai Legal QA Framework Using Large Language Models and Mixed Legal Datasets.

.- Leveraging Generative AI for Chronic Kidney Disease Prediction: A Comparative Study of Open-Source LLMs.

.- LADDER: Latent Attention and Decomposition for Deep Enhanced Retrieval in Medical Question-Answering Systems.

.- Test It Before You Trust It: Applying Software Testing for Trustworthy In-context Learning.

.- CAFE: Context-aware Applicability-weighted Fairness Evaluation.

.- A Survey of LoRA Algorithm Variations for Language Models.

.- ViMRHP: A Vietnamese Benchmark Dataset for Multimodal Review Helpfulness Prediction via Human-AI Collaborative Annotation.

.-Towards an Ontological Approach to Browser Fingerprinting Detection and Privacy Risk Assessment.

.- Dual-Style Transcription of Historical Manuscripts based on Multimodal Small Language Models with Switchable Adapters.

.- Semantic Reasoning for Device Identification in Dynamic Internet of Things Environments with Large Language Model.

.- Instruction Tuning TextFlow Semi-Automatic RFCs Generation.

.- News Timeline Summarization: Recent Methods.

.- QA2HALL: A Framework for Generating Non-trivial Hallucination Detection Datasets from KGQA Datasets.

.- Causal Relation-Aware Data Augmentation for Legal Textual Entailment.

.- Improve Smart Contract Vulnerability Explanation with Synthetic Data and Chain-of-Thought Prompting.

.- The benefits of query-based KGQA systems for complex and temporal questions in LLM era.

.- TopicVD: A Topic-Based Dataset of Video-Guided Multimodal Machine Translation for Documentaries.

.- Multimodal Event Detection: Current Approaches and Defining the New Playground through LLMs and VLMs.

.- ReSHQ: Re-ranking Based on the Similarity between Hypothetical Query and Query.

.- Multi-hop Question Answering in SlideVQA Based on Beam Retrieval.