惯性聚合 高效追踪和阅读你感兴趣的博客、新闻、科技资讯
阅读原文 在惯性聚合中打开

推荐订阅源

小众软件
小众软件
B
Blog RSS Feed
美
美团技术团队
博
博客园 - 【当耐特】
C
Check Point Blog
freeCodeCamp Programming Tutorials: Python, JavaScript, Git & More
M
MIT News - Artificial intelligence
aimingoo的专栏
aimingoo的专栏
J
Java Code Geeks
Cyber Security Advisories - MS-ISAC
Cyber Security Advisories - MS-ISAC
Microsoft Azure Blog
Microsoft Azure Blog
博
博客园 - 司徒正美
T
Tailwind CSS Blog
Last Week in AI
Last Week in AI
Google DeepMind News
Google DeepMind News
D
DataBreaches.Net
人人都是产品经理
人人都是产品经理
N
Netflix TechBlog - Medium
Vercel News
Vercel News
P
Proofpoint News Feed
IT之家
IT之家
I
InfoQ
腾
腾讯CDC
H
Hackread – Cybersecurity News, Data Breaches, AI and More

Proceedings of the AAAI Conference on Artificial Intelligence

Resource Efficient Sleep Staging via Multi-Level Masking and Prompt Learning AutoMalDesc: Large-Scale Script Analysis for Cyber Threat Research Modulation-Based Backdoors: Leveraging Amplitude and Frequency Patterns to Attack Speaker Recognition Learning Structurally Stabilized Representations for Lossless DNA Storage ViG-RAG: Video-aware Graph Retrieval-Augmented Generation via Temporal and Semantic Hybrid Reasoning Transferable Backdoor Attacks for Code Models via Sharpness-Aware Adversarial Perturbation Toward Multimodal Fake News Detection by Multi-perspective Rationale Generation and Verification RTMol: Rethinking Molecule-text Alignment in a Round-trip View Physical-regularized Hierarchical Generative Model for Metallic Glass Structural Generation and Energy Prediction Regressor-guided Diffusion Model for De Novo Peptide Sequencing with Explicit Mass Control RareAgents: Autonomous Multi-disciplinary Team for Rare Disease Diagnosis and Treatment Transferring Causal Driving Patterns for Generalizable Traffic Simulation with Diffusion-Based Distillation TRACE: Transformation-Aware Graph Refinement for Reaction Condition Prediction SIDE: Surrogate Conditional Data Extraction from Diffusion Models DyC-STG: Dynamic Causal Spatio-Temporal Graph Network for Real-time Data Credibility Analysis in IoT ProAR: Probabilistic Autoregressive Modeling for Molecular Dynamics Light but Sharp: SlimSTAD for Real-Time Action Detection from Sensor Data VFCionX: Bridging Large and Small Models for Robust Vulnerability-Fixing Commit Identification T2Agent: A Tool-augmented Multimodal Misinformation Detection Agent with Monte Carlo Tree Search Measuring What Matters: Scenario-Driven Evaluation for Trajectory Predictors in Autonomous Driving DensiCrafter: Physically-Constrained Generation and Fabrication of Self-Supporting Hollow Structures Topology-Enhanced and Label Correlation-Aware Model for Protein-Protein Interaction Prediction NucEL: Single-Nucleotide ELECTRA-Style Genomic Pre-training for Efficient and Interpretable Representations OR-R1: Automating Modeling and Solving of Operations Research Optimization Problem via Test-Time Reinforcement Learning Learning from Long-Term Engagement: Adaptive Tutoring Dialogue Planning for Personalized Education Toward Time-Continuous Data Inference in Sparse Urban CrowdSensing Multi-Horizon Time Series Forecasting of Non-Parametric CDFs with Deep Lattice Networks From Stimuli to Minds: Enhancing Psychological Reasoning in LLMs via Bilateral Reinforcement Learning Unveiling the Attribute Misbinding Threat in Identity-Preserving Models DeepSenseMoE: Harnessing Power of Time Series Foundation Models for Few-Shot Human Activity Recognition
InteChar: A Unified Oracle Bone Character List for Ancien...
Xiaolei Diao · 2026-03-14 · via Proceedings of the AAAI Conference on Artificial Intelligence

Authors

  • Xiaolei Diao College of Computer Science and Technology, Jilin University Key Laboratory of Ancient Chinese Script, Culture Relics and Artificial Intelligence, Jilin University Department of information engineering and computer science, University of Trento
  • Zhihan Zhou College of Computer Science and Technology, Jilin University Key Laboratory of Ancient Chinese Script, Culture Relics and Artificial Intelligence, Jilin University
  • Lida Shi School of Artificial Intelligence, Jilin University Key Laboratory of Ancient Chinese Script, Culture Relics and Artificial Intelligence, Jilin University
  • Ting Wang School of Software Engineering, Tongji University
  • Ruihua Qi School of Archaeology, Jilin University Key Laboratory of Ancient Chinese Script, Culture Relics and Artificial Intelligence, Jilin University
  • Daqian Shi Queen Mary, University of London Key Laboratory of Ancient Chinese Script, Culture Relics and Artificial Intelligence, Jilin University
  • Hao Xu College of Computer Science and Technology, Jilin University Key Laboratory of Ancient Chinese Script, Culture Relics and Artificial Intelligence, Jilin University

DOI:

https://doi.org/10.1609/aaai.v40i1.36981

Abstract

Constructing historical language models (LMs) plays a crucial role in aiding archaeological provenance studies and understanding ancient cultures. However, existing resources present major challenges for training effective LMs on historical texts. First, the scarcity of historical language samples renders unsupervised learning approaches based on large text corpora highly inefficient, hindering effective pre-training. Moreover, due to the considerable temporal gap and complex evolution of ancient scripts, the absence of comprehensive character encoding schemes limits the digitization and computational processing of ancient texts, particularly in early Chinese writing. To address these challenges, we introduce InteChar, a unified and extensible character list that integrates unencoded oracle bone characters with traditional and modern Chinese. InteChar enables consistent digitization and representation of historical texts, providing a foundation for robust modeling of ancient scripts. To evaluate the effectiveness of InteChar, we construct the Oracle Corpus Set (OracleCS), an ancient Chinese corpus that combines expert-annotated samples with LLM-assisted data augmentation, centered on Chinese oracle bone inscriptions. Extensive experiments show that models trained with InteChar on OracleCS achieve substantial improvements across various historical language understanding tasks, confirming the effectiveness of our approach and establishing a solid foundation for future research in ancient Chinese NLP.

How to Cite

Diao, X., Zhou, Z., Shi, L., Wang, T., Qi, R., Shi, D., & Xu, H. (2026). InteChar: A Unified Oracle Bone Character List for Ancient Chinese Language Modeling. Proceedings of the AAAI Conference on Artificial Intelligence, 40(1), 211–219. https://doi.org/10.1609/aaai.v40i1.36981

Issue

Section

AAAI Technical Track on Application Domains I

此内容由惯性聚合(RSS阅读器)自动聚合整理，仅供阅读参考。 原文来自 — 版权归原作者所有。