| 查看: 835 | 回复: 3 | |||
| 当前只显示满足指定条件的回帖，点击这里查看本话题的所有回帖 | |||
time88木虫之王 (文学泰斗)
|
[求助]
请查一下论文被SCI和EI检索的情况
|
||
|
请给我查一下2012发表论文被SCI和EI检索的情况。作者为yuan lichi,或 yuan li-chi, 或 lichi yuan。谢谢！ |
回复此楼» 猜你喜欢
朋友圈看到的
已经有4人回复
-
明天放榜？
已经有5人回复
-
时间戳变了，能看出什么问题？
已经有11人回复
-
欢迎发来filecode的Mz6后的代码验证其规律
已经有109人回复
-
重要消息，中午系统在维护
已经有10人回复
-
两姐妹，一个辛辛苦苦30岁工作当大学教师，另一个23岁就工作当开车教练
已经有7人回复
-
投稿咨询
已经有6人回复
-
时间戳又变了8-15
已经有26人回复
-
今天维护系统维护 祝所有人 高中
已经有9人回复
-
哪位老哥知道今年的国自然具体哪一天放榜？
已经有16人回复
» 本主题相关价值贴推荐，对您同样有帮助:
- 求助大家有没有管理学方面被EI和SCI检索的国外期刊啊
已经有8人回复
- 求助查询一篇文章的SCI与EI收录号及收录页面文件
已经有6人回复
- 被SCI和EI检索的论文从哪里可以下载【求助完结】
已经有10人回复
- 急求文章的EI检索号(1篇含SCI号），
已经有6人回复
- SCI/EI怎么检索收录的中文文献
已经有4人回复
- 关于EI和SCI论文问题的探讨
已经有47人回复
- 急用！！麻烦能查检索的楼主帮忙检索一下我的7篇文章是否被SCI、EI检索，十分感谢！
已经有7人回复
- 《四川大学工程学报》是SCI还是EI？
已经有22人回复
ruzjtrb138
禁虫 (初入文坛)
|
本帖内容被屏蔽 |
4楼2017-05-26 11:34:50
★
杈杈: 金币+1, 感谢提供检索信息！ 2012-12-14 09:57:16
杈杈: 金币+1, 感谢提供检索信息！ 2012-12-14 09:57:16
|
牛，瞅着眼红 1. A lexicalized syntactic parsing model based on valence structure Yuan, Li-Chi (Jiangxi Key Laboratory of Date and Knowledge Engineering, School of Information Technology, Jiangxi University of Finance and Economics, Nanchang 330013, China) Source: Zhongnan Daxue Xuebao (Ziran Kexue Ban)/Journal of Central South University (Science and Technology), v 43, n 5, p 1808-1813, May 2012 Language: Chinese Database: Compendex Abstract | Detailed | | | FULL TEXT LINKS 2. Vari-gram language model based on word clustering Yuan, Li-Chi (School of Information Technology, Jiangxi University of Finance and Economics, Nanchang 330013, China) Source: Journal of Central South University of Technology (English Edition), v 19, n 4, p 1057-1062, April 2012 Database: Compendex Abstract | Detailed | | | | FULL TEXT LINKS 3. A part-of-speech tagging method based on improved hidden Markov model Yuan, Li-Chi (Jiangxi Key Lab. of Data and Knowledge Engineering, School of Information Technology, Jiangxi University of Finance and Economics, Nanchang 330013, China) Source: Zhongnan Daxue Xuebao (Ziran Kexue Ban)/Journal of Central South University (Science and Technology), v 43, n 8, p 3053-3057, August 2012 Language: Chinese Database: Compendex Abstract | Detailed | | | FULL TEXT LINKS 4. Statistical parsing with linguistic features Yuan, Li-Chi (School of Information Technology, Jiangxi University of Finance and Economics, Nanchang 330013, China) Source: Zhongnan Daxue Xuebao (Ziran Kexue Ban)/Journal of Central South University (Science and Technology), v 43, n 3, p 986-991, March 2012 Language: Chinese Database: Compendex Abstract | Detailed | | | FULL TEXT LINKS 5. Improved hidden Markov model for speech recognition and POS tagging Yuan, Li-Chi (School of Information Technology, Jiangxi University of Finance and Economics, Nanchang 330013, China) Source: Journal of Central South University of Technology (English Edition), v 19, n 2, p 511-516, February 2012 Database: Compendex Abstract | Detailed | | | | FULL TEXT LINKS |
2楼2012-12-14 09:35:43
chuandanwei
木虫 (著名写手)
- 应助: 53 (初中生)
- 金币: 5124.6
- 散金: 190
- 红花: 6
- 帖子: 1927
- 在线: 234.2小时
- 虫号: 1455378
- 注册: 2011-10-22
- 专业: 电化学分析
【答案】应助回帖
★
杈杈: 金币+1, 感谢应助！ 2012-12-14 09:57:28
杈杈: 金币+1, 感谢应助！ 2012-12-14 09:57:28
|
SCI 检索情况（好像2012只有两篇） 标题: Vari-gram language model based on word clustering 作者: Yuan Li-chi 来源出版物: JOURNAL OF CENTRAL SOUTH UNIVERSITY OF TECHNOLOGY 卷: 19 期: 4 页: 1057-1062 DOI: 10.1007/s11771-012-1109-z 出版年: APR 2012 被引频次: 0 (来自 Web of Science) Vari-gram language model based on word clustering 作者: Yuan, LC (Yuan Li-chi)1,2 来源出版物: JOURNAL OF CENTRAL SOUTH UNIVERSITY OF TECHNOLOGY 卷: 19 期: 4 页: 1057-1062 DOI: 10.1007/s11771-012-1109-z 出版年: APR 2012 被引频次: 0 (来自 Web of Science) 引用的参考文献: 18 [ 查看 Related Records ] 引证关系图 摘要: Category-based statistic language model is an important method to solve the problem of sparse data. But there are two bottlenecks: 1) The problem of word clustering. It is hard to find a suitable clustering method with good performance and less computation. 2) Class-based method always loses the prediction ability to adapt the text in different domains. In order to solve above problems, a definition of word similarity by utilizing mutual information was presented. Based on word similarity, the definition of word set similarity was given. Experiments show that word clustering algorithm based on similarity is better than conventional greedy clustering method in speed and performance, and the perplexity is reduced from 283 to 218. At the same time, an absolute weighted difference method was presented and was used to construct vari-gram language model which has good prediction ability. The perplexity of vari-gram model is reduced from 234.65 to 219.14 on Chinese corpora, and is reduced from 195.56 to 184.25 on English corpora compared with category-based model. 入藏号: WOS:000302249800026 文献类型: Article 语种: English 作者关键词: word similarity; word clustering; statistical language model; vari-gram language model 通讯作者地址: Yuan, LC (通讯作者),Jiangxi Univ Finance & Econ, Sch Informat Technol, Nanchang 330013, Peoples R China. 地址: 1. Jiangxi Univ Finance & Econ, Sch Informat Technol, Nanchang 330013, Peoples R China 2. Cent S Univ, Sch Informat Sci & Engn, Changsha 410083, Peoples R China 电子邮件地址: yuanlichi@sohu.com 标题: Improved hidden Markov model for speech recognition and POS tagging 作者: Yuan Li-chi 来源出版物: JOURNAL OF CENTRAL SOUTH UNIVERSITY OF TECHNOLOGY 卷: 19 期: 2 页: 511-516 DOI: 10.1007/s11771-012-1033-2 出版年: FEB 2012 被引频次: 0 (来自 Web of Science) Improved hidden Markov model for speech recognition and POS tagging 作者: Yuan, LC (Yuan Li-chi)1,2 来源出版物: JOURNAL OF CENTRAL SOUTH UNIVERSITY OF TECHNOLOGY 卷: 19 期: 2 页: 511-516 DOI: 10.1007/s11771-012-1033-2 出版年: FEB 2012 被引频次: 0 (来自 Web of Science) 引用的参考文献: 26 [ 查看 Related Records ] 引证关系图 摘要: In order to overcome defects of the classical hidden Markov model (HMM), Markov family model (MFM), a new statistical model was proposed. Markov family model was applied to speech recognition and natural language processing. The speaker independently continuous speech recognition experiments and the part-of-speech tagging experiments show that Markov family model has higher performance than hidden Markov model. The precision is enhanced from 94.642% to 96.214% in the part-of-speech tagging experiments, and the work rate is reduced by 11.9% in the speech recognition experiments with respect to HMM baseline system. 入藏号: WOS:000299928600030 文献类型: Article 语种: English 作者关键词: hidden Markov model; Markov family model; speech recognition; part-of-speech tagging 通讯作者地址: Yuan, LC (通讯作者),Jiangxi Univ Finance & Econ, Sch Informat Technol, Nanchang 330013, Peoples R China. 地址: 1. Jiangxi Univ Finance & Econ, Sch Informat Technol, Nanchang 330013, Peoples R China 2. Cent S Univ, Sch Informat Sci & Engn, Changsha 410083, Peoples R China 电子邮件地址: yuanlichi@sohu.com |
3楼2012-12-14 09:49:48
