版权说明 操作指南
首页 > 成果 > 详情

Learning semantic representation with neural networks for community question answering retrieval

认领
导出
Link by 万方学术期刊
反馈
分享
QQ微信 微博
成果类型:
期刊论文
作者:
Zhou, Guangyou*;Zhou, Yin;He, Tingting(何婷婷);Wu, Wensheng
通讯作者:
Zhou, Guangyou
作者机构:
[He, Tingting; Zhou, Guangyou; Zhou, Yin] Cent China Normal Univ, Sch Comp, Wuhan 430079, Peoples R China.
[Wu, Wensheng] Univ So Calif, Dept Comp Sci, Los Angeles, CA 90089 USA.
通讯机构:
[Zhou, Guangyou] C
Cent China Normal Univ, Sch Comp, Wuhan 430079, Peoples R China.
语种:
英文
关键词:
Community question answering;Question retrieval;Text mining;Yahoo! Answers
期刊:
Knowledge-Based Systems
ISSN:
0950-7051
年:
2016
卷:
93
期:
Feb.1
页码:
75-83
基金类别:
National Natural Science Foundation of ChinaNational Natural Science Foundation of China (NSFC) [61303180, 61573163]; Beijing Natural Science FoundationBeijing Natural Science Foundation [4144087]; CCF-Tencent Open Research Fund
机构署名:
本校为第一且通讯机构
院系归属:
计算机学院
摘要:
Learning the semantic representation using neural network architecture.The neural network is trained via pre-training and fine-tuning phase.The learned semantic level feature is incorporated into a LTR framework. In community question answering (cQA), users pose queries (or questions) on portals like Yahoo! Answers which can then be answered by other users who are often knowledgeable on the subject. cQA is increasingly popular on the Web, due to its convenience and effectiveness in connecting users with queries and those with answers. In this article, we study the problem of finding previous q...

反馈

验证码:
看不清楚,换一个
确定
取消

成果认领

标题:
用户 作者 通讯作者
请选择
请选择
确定
取消

提示

该栏目需要登录且有访问权限才可以访问

如果您有访问权限,请直接 登录访问

如果您没有访问权限,请联系管理员申请开通

管理员联系邮箱:yun@hnwdkj.com