找回密码
 To register

QQ登录

只需一步,快速开始

扫一扫,访问微社区

Titlebook: Natural Language Processing and Chinese Computing; 12th National CCF Co Fei Liu,Nan Duan,Yu Hong Conference proceedings 2023 The Editor(s)

[复制链接]
楼主: Madison
发表于 2025-3-28 14:51:15 | 显示全部楼层
发表于 2025-3-28 19:11:20 | 显示全部楼层
发表于 2025-3-29 01:26:22 | 显示全部楼层
发表于 2025-3-29 03:20:24 | 显示全部楼层
Multi-perspective Feature Fusion for Event-Event Relation Extractionderstand the relation structure of the event chain in a document. Existing methods mainly focused on single-event relations, such as causality, while only utilizing trigger words or semantic span as model inputs, ignoring the impact of event arguments and contextual features on events. Hence, we pro
发表于 2025-3-29 09:11:25 | 显示全部楼层
发表于 2025-3-29 14:35:47 | 显示全部楼层
A Relational Classification Network Integrating Multi-scale Semantic Features provide high-quality corpus in fields such as machine translation, structured data generation, knowledge graphs, and semantic question answering. Existing relational classification models include models based on traditional machine learning, models based on deep learning, and models based on attent
发表于 2025-3-29 18:20:57 | 显示全部楼层
发表于 2025-3-29 21:46:48 | 显示全部楼层
发表于 2025-3-30 02:28:57 | 显示全部楼层
Collective Entity Linking with Joint Subgraphsel correlation of linking decisions between different mentions. In this paper, we propose three ideas: (i) build subgraphs made up of partial mentions instead of those in the entire document to improve computation efficiency, (ii) perform joint disambiguation over context and knowledge base (KB), an
发表于 2025-3-30 06:26:04 | 显示全部楼层
Coarse-to-Fine Entity Representations for Document-Level Relation Extractions, usually constructing a document-level graph that captures document-aware interactions, can obtain useful entity representations thus helping tackle document-level RE. These methods either focus more on the entire graph, or pay more attention to a part of the graph, e.g., paths between the target
 关于派博传思  派博传思旗下网站  友情链接
派博传思介绍 公司地理位置 论文服务流程 影响因子官网 SITEMAP 大讲堂 北京大学 Oxford Uni. Harvard Uni.
发展历史沿革 期刊点评 投稿经验总结 SCIENCEGARD IMPACTFACTOR 派博系数 清华大学 Yale Uni. Stanford Uni.
|Archiver|手机版|小黑屋| 派博传思国际 ( 京公网安备110108008328) GMT+8, 2025-5-12 08:25
Copyright © 2001-2015 派博传思   京公网安备110108008328 版权所有 All rights reserved
快速回复 返回顶部 返回列表