Titlebook: Natural Language Processing and Chinese Computing; 13th National CCF Co Derek F. Wong,Zhongyu Wei,Muyun Yang Conference proceedings 2025 Th

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書目名稱Natural Language Processing and Chinese Computing影響因子(影響力)




書目名稱Natural Language Processing and Chinese Computing影響因子(影響力)學(xué)科排名




書目名稱Natural Language Processing and Chinese Computing網(wǎng)絡(luò)公開度




書目名稱Natural Language Processing and Chinese Computing網(wǎng)絡(luò)公開度學(xué)科排名




書目名稱Natural Language Processing and Chinese Computing被引頻次




書目名稱Natural Language Processing and Chinese Computing被引頻次學(xué)科排名




書目名稱Natural Language Processing and Chinese Computing年度引用




書目名稱Natural Language Processing and Chinese Computing年度引用學(xué)科排名




書目名稱Natural Language Processing and Chinese Computing讀者反饋




書目名稱Natural Language Processing and Chinese Computing讀者反饋學(xué)科排名

CHARM

洞察力

Contrastive Learning-Based Sequential Recommendation Modeltems in sequential recommendation. The proposed contrastive learning framework effectively captures intra-sequence item transition patterns and inter-sequence dependencies among items. Empirical evaluations on real-world datasets show that our model significantly outperforms advanced baseline models

Dislocation

tariff

Enhancing Complex Causality Extraction via?Improved Subtask Interaction and?Knowledge Fusionween the two ECE subtasks. Besides, we design a knowledge fusion mechanism to fuse knowledge in the two modalities. Furthermore, we employ separate decoders for each subtask to facilitate complex causality extraction. Experiments on three benchmark datasets demonstrate that our method achieves state

單純

Mathematical Reasoning via?Multi-step Self Questioning and?Answering for?Small Language Modelslti-step reason in a self questioning and answering way and answer each sub-question in a single step iteratively. Experiments on current mathematical reasoning tasks demonstrate the effectiveness of the proposed approach.

貞潔

High-Quality Distractors Generation for?Human Exam Based on?Reinforcement Learning from?Preference Fment learning, we build and train a reward model to evaluate the quality of individual distractors. Combining the reward model with a diversity evaluation metric, we design an objective function and further train the fine-tuned model using reinforcement learning. Experiments show that the DGRL, afte

disparage

使顯得不重要

MANet: A Multiview Attention Network for?Automatic ICD Codingre integrated to effectively fuse these multiview features, generating more informative and discriminative representations. Extensive experiments conducted on the popular MIMIC-III and MIMIC-IV-ICD9 datasets demonstrate the superiority of our proposed MANet over state-of-the-art methods. On MIMIC-II

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