發(fā)布時間：2018-05-20 10:19

  本文選題：中文人名識別 + 詞向量　； 參考：《大連理工大學》2016年碩士論文

【摘要】：中文人名識別任務是中文信息處理領域中的基礎任務,其性能的好壞將直接影響到其他任務的性能。中文人名的隨意性使其在未登錄詞中占有較大的比重,解決未登錄詞識別問題首先要解決人名識別問題。因此,解決中文人名識別問題具有重要的意義�，F有基于統(tǒng)計的中文人名識別方法存在特征選取復雜和人工干預等問題,針對這些問題,本文提出了一種基于循環(huán)神經網絡(Recurrent Neural Networks)的中文人名識別方法,該方法僅采用詞向量作為模型的特征且無需人工干預,有效降低了特征選取的復雜性和人工干預對實驗造成的影響。此外,詞向量可以通過大量未標注的中文數據訓練獲得,然后將蘊含豐富語義信息的詞向量作為循環(huán)神經網絡模型的輸入,可以使模型學習到更多的信息,提升模型的性能。本文將模型分為兩個階段：模型構建階段和后處理階段。在模型構建階段,我們將重點放在詞向量的優(yōu)化策略上。針對詞向量的優(yōu)化問題,本文提出了三種策略：(1)將word2vec訓練得到的詞向量替換循環(huán)神經網絡模型的隨機初始詞向量(2)對詞向量訓練語料進行數詞泛化操作(3)改進word2vec模型,將特征信息融入詞向量實驗結果表明,通過詞向量的優(yōu)化操作,中文人名識別模型的F值提高了2.23%。在后處理階段,通過上下文規(guī)則對候選人名進行過濾；采用基于篇章的全局擴散操作召回在某一位置由于信息不足識別不出而在其他位置能夠被識別的人名；使用基于篇章的局部擴散操作識別篇章信息中有名無姓或者有姓無名的人名。實驗結果表明,通過規(guī)則過濾和擴散操作,中文人名識別模型的F值提高了4.74%。
[Abstract]:The task of Chinese name recognition is the basic task in the field of Chinese information processing, and its performance will directly affect the performance of other tasks. The randomness of Chinese names makes them occupy a large proportion in unrecorded words. To solve the problem of unrecorded words recognition, we must first solve the problem of personal name recognition. Therefore, it is of great significance to solve the problem of Chinese name recognition. The existing Chinese name recognition methods based on statistics have the problems of complex feature selection and artificial intervention. In view of these problems, this paper proposes a Chinese name recognition method based on cyclic neural network (Recurrent Neural Network). This method only uses word vector as the feature of the model and does not need human intervention, which effectively reduces the complexity of feature selection and the influence of artificial intervention on the experiment. In addition, the word vector can be obtained through a large number of unlabeled Chinese data training, and then the word vector with rich semantic information can be used as the input of the cyclic neural network model, so that the model can learn more information and improve the performance of the model. This paper divides the model into two stages: model construction stage and post-processing phase. In the stage of model construction, we focus on the optimization strategy of word vector. To solve the problem of word vector optimization, this paper proposes three strategies: 1) the word vector is replaced by the random initial word vector of the neural network model, which is trained by word2vec, and the random initial word vector is used to generalize the word vector training corpus. (3) the word2vec model is improved. The experimental results show that the F value of the Chinese name recognition model is increased by 2.233 by the optimization of the word vector. In the post-processing stage, the candidate's name is filtered by contextual rules, and the text based global diffusion operation is used to recall the names of people who can be recognized in other places because of the lack of information. A text-based local diffusion operation is used to identify a person with no or no name in the text information. The experimental results show that the F value of the Chinese name recognition model is increased by 4.74 by regular filtering and diffusion operation.
【學位授予單位】：大連理工大學
【學位級別】：碩士
【學位授予年份】：2016
【分類號】：TP391.1;TP183

【參考文獻】

相關期刊論文 前10條

1 王s，

本文編號：1914230