找回密碼
 To register

QQ登錄

只需一步,快速開始

掃一掃,訪問微社區(qū)

打印 上一主題 下一主題

Titlebook: Web and Big Data; 8th International Jo Wenjie Zhang,Anthony Tung,Hongjie Guo Conference proceedings 2024 The Editor(s) (if applicable) and

[復(fù)制鏈接]
樓主: dejected
41#
發(fā)表于 2025-3-28 16:01:26 | 只看該作者
42#
發(fā)表于 2025-3-28 22:38:46 | 只看該作者
0302-9743 e on Web and Big Data, APWeb-WAIM 2024, held in Jinhua, China, during August 30–September 1, 2024...The 171 full papers presented in these proceedings were carefully reviewed and selected from 558 submissions...The papers are organized in the following topical sections:.Part I:?Natural language proc
43#
發(fā)表于 2025-3-28 23:24:13 | 只看該作者
44#
發(fā)表于 2025-3-29 03:28:11 | 只看該作者
45#
發(fā)表于 2025-3-29 09:34:21 | 只看該作者
A Boundary Feature Enhanced Span-Based Nested Named Entity Recognition Method, to improve the efficiency of BFSN2ER, we introduce a multi-task learning framework to achieve jointly models training. To validate the performance of BFSN2ER, experiments were conducted on three large datasets. Comparing with seven baselines, BFSN2ER?achieved obviously better recall and F1-score,
46#
發(fā)表于 2025-3-29 11:33:52 | 只看該作者
47#
發(fā)表于 2025-3-29 18:03:20 | 只看該作者
CeER: A Nested Name Entity Recognition Model Incorporating Gaze Features which reflect their importance in the reading cognitive process. Finally, we utilize the encoder improved by gaze feature learning and follow the question-answering architecture to identify all possible nested entities. We select three public eye-tracking datasets and two nested NER datasets, GENI
48#
發(fā)表于 2025-3-29 22:10:51 | 只看該作者
Joint Semantic Relation Extraction for Multiple Entity Packetsng the fluctuations and regular semantics of entities. Finally, we aggregate the joint willingness among the entities in packets by combining the above two types of features, and thus extract the joint semantic relations effectively. Experimental results on various datasets illustrate that our metho
49#
發(fā)表于 2025-3-30 00:11:44 | 只看該作者
50#
發(fā)表于 2025-3-30 05:13:00 | 只看該作者
Explicit Relation-Enhanced AMR for?Document-Level Event Argument Extraction with?Global-Local Attentles and trigger interaction. This module also improves the model’s efficiency in resource allocation and enables a more refined focus on relational data, which optimizes performance in event argument extraction. Empirical evidence from experiments conducted on WIKIEVENTS shows that our model, enhanc
 關(guān)于派博傳思  派博傳思旗下網(wǎng)站  友情鏈接
派博傳思介紹 公司地理位置 論文服務(wù)流程 影響因子官網(wǎng) 吾愛論文網(wǎng) 大講堂 北京大學(xué) Oxford Uni. Harvard Uni.
發(fā)展歷史沿革 期刊點評 投稿經(jīng)驗總結(jié) SCIENCEGARD IMPACTFACTOR 派博系數(shù) 清華大學(xué) Yale Uni. Stanford Uni.
QQ|Archiver|手機版|小黑屋| 派博傳思國際 ( 京公網(wǎng)安備110108008328) GMT+8, 2026-1-20 08:36
Copyright © 2001-2015 派博傳思   京公網(wǎng)安備110108008328 版權(quán)所有 All rights reserved
快速回復(fù) 返回頂部 返回列表
上蔡县| 榆树市| 招远市| 阿城市| 沙河市| 社会| 五寨县| 泰宁县| 佛教| 兴宁市| 九台市| 陵川县| 沁源县| 青州市| 古浪县| 阳曲县| 红河县| 邛崃市| 北京市| 佳木斯市| 衡阳县| 新化县| 郸城县| 酉阳| 辽源市| 海晏县| 兖州市| 黄骅市| 鄢陵县| 林周县| 张家口市| 定陶县| 望谟县| 平凉市| SHOW| 新巴尔虎右旗| 咸丰县| 白玉县| 织金县| 礼泉县| 金沙县|