2023年全國(guó)碩士研究生考試考研英語(yǔ)一試題真題(含答案詳解+作文范文)_第1頁(yè)
已閱讀1頁(yè),還剩50頁(yè)未讀, 繼續(xù)免費(fèi)閱讀

下載本文檔

版權(quán)說(shuō)明:本文檔由用戶(hù)提供并上傳,收益歸屬內(nèi)容提供方,若內(nèi)容存在侵權(quán),請(qǐng)進(jìn)行舉報(bào)或認(rèn)領(lǐng)

文檔簡(jiǎn)介

1、合肥工業(yè)大學(xué)碩士學(xué)位論文中文自動(dòng)分詞及人名識(shí)別技術(shù)研究姓名:蔣才智申請(qǐng)學(xué)位級(jí)別:碩士專(zhuān)業(yè):計(jì)算機(jī)軟件與理論指導(dǎo)教師:王浩2011-042 Research of Automatic Chinese segmentation and name recognition Abstract With the continuous development of national information technology and the pop

2、ularization of Internet, natural language understanding becomes a hot research field. As the first step in natural language understanding, automatic Chinese segmentation is more sophisticated and it determines the follow

3、-up processes. Chinese name is the most important component of unknown words, its existence is one of the most important factors which are influence the segmentation accuracy. Therefore, Chinese name recognition is a key

4、 technology in Chinese automatic segmentation. Currently, it is still unsatisfactory in processing on the result, its recognition quality still need to be enhanced in the further. This thesis will research Chinese autom

5、atic segmentation model and Chinese name identify problems, mainly work focused on: (1)A new dictionary mechanism named Dynamic four-character bidirectional dictionary mechanism is proposed. In this dictionary mechanism,

6、 we can reduce the mean frequency of visiting dictionary effectively. (2) In order to boost the precision of Chinese name recognition, we construct a Chinese name recognition model combining HowNet with Bayesian classifi

7、er. The basic idea is to locate and recognize the Chinese name roughly by Bayesian classifier, and then to fix this name by using HowNet. The model not only has the advantages of simple formula and ability to learn, but

8、also overcomes the extensive use of rules and the difficulty of boundary defining. Keywords: natural language understanding; Automatic Chinese segmentation; four-character dictionary; Chinese name recognition; Bayesian c

溫馨提示

  • 1. 本站所有資源如無(wú)特殊說(shuō)明,都需要本地電腦安裝OFFICE2007和PDF閱讀器。圖紙軟件為CAD,CAXA,PROE,UG,SolidWorks等.壓縮文件請(qǐng)下載最新的WinRAR軟件解壓。
  • 2. 本站的文檔不包含任何第三方提供的附件圖紙等,如果需要附件,請(qǐng)聯(lián)系上傳者。文件的所有權(quán)益歸上傳用戶(hù)所有。
  • 3. 本站RAR壓縮包中若帶圖紙,網(wǎng)頁(yè)內(nèi)容里面會(huì)有圖紙預(yù)覽,若沒(méi)有圖紙預(yù)覽就沒(méi)有圖紙。
  • 4. 未經(jīng)權(quán)益所有人同意不得將文件中的內(nèi)容挪作商業(yè)或盈利用途。
  • 5. 眾賞文庫(kù)僅提供信息存儲(chǔ)空間,僅對(duì)用戶(hù)上傳內(nèi)容的表現(xiàn)方式做保護(hù)處理,對(duì)用戶(hù)上傳分享的文檔內(nèi)容本身不做任何修改或編輯,并不能對(duì)任何下載內(nèi)容負(fù)責(zé)。
  • 6. 下載文件中如有侵權(quán)或不適當(dāng)內(nèi)容,請(qǐng)與我們聯(lián)系,我們立即糾正。
  • 7. 本站不保證下載資源的準(zhǔn)確性、安全性和完整性, 同時(shí)也不承擔(dān)用戶(hù)因使用這些下載資源對(duì)自己和他人造成任何形式的傷害或損失。

評(píng)論

0/150

提交評(píng)論