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time88木虫之王 (文学泰斗)
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[求助]
请查一下论文被SCI和EI检索的情况
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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！ |
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ruzjtrb138
禁虫 (初入文坛)
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本帖内容被屏蔽 |
4楼2017-05-26 11:34:50
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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
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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
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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
