本文選題：語義相關(guān)度 + 激活力��； 參考：《北京郵電大學(xué)》2013年碩士論文

【摘要】：語義相關(guān)度分析足自然語言處理領(lǐng)域的一項基本研究內(nèi)容,是文本智能化處理和分析的關(guān)鍵技術(shù),主要研究的是文本中詞語之間語義關(guān)聯(lián)程度。語義相關(guān)度分析可以有效改善傳統(tǒng)文本處理分析中忽略了文本中詞語之間的語義關(guān)聯(lián)的問題,本文主要研究的是基于語料庫的詞語語義相關(guān)度計算,及其在文本智能處理中應(yīng)用。 論文首先對文本中詞語語義相關(guān)度分析相關(guān)技術(shù)進行了深入調(diào)研,分析了現(xiàn)有語義分析技術(shù)的發(fā)展現(xiàn)狀和應(yīng)用方向,比較了現(xiàn)有各種分析計算方法的優(yōu)缺點。在此基礎(chǔ)上,本文完成重點創(chuàng)新工作和主要研究成果包括如下三個方面： 1.基于激活力復(fù)雜網(wǎng)絡(luò)模型,利用詞語在上下文語境中的共現(xiàn)關(guān)系,提出一種動態(tài)詞語義網(wǎng)絡(luò)(DWSN, Dynamic Word Semantic Network)的構(gòu)建方法,用于分析特定的應(yīng)用環(huán)境下詞語之間的語義相關(guān)度。實驗表明,與現(xiàn)有的基于語料庫的語義相關(guān)度分析方法相比,動態(tài)詞網(wǎng)絡(luò)算法不論從語義分析的準(zhǔn)確性,還是從算法的效率上都有比較大的改進。 2.基于上述DWSN算法,提出了基于語義分析的實體關(guān)系分析方法,挖掘命名實體隱含在其相關(guān)上下文中的潛在關(guān)系。該算法已用于校園信息垂直搜索引擎COSE中,用于學(xué)校老師潛在社交關(guān)系的挖掘與展示。 3.基于DWSN算法,提出了基于語義分析的特征選擇遷移學(xué)習(xí)算法。通過選取訓(xùn)練樣本和測試樣本中語義一致的特征作為分類時采用的特征,以解決文本分類過程中訓(xùn)練樣本和測試樣本特征空間不一致的問題。實驗表明我們提出的算法相對傳統(tǒng)分類算法可以提高10%-20%的分類準(zhǔn)確率
[Abstract]:Semantic relevance analysis is a basic research content in the field of natural language processing, which is the key technology of text intelligent processing and analysis.Semantic relevance analysis can effectively improve the problem of semantic relevance between words in traditional text processing analysis. In this paper, we mainly study the calculation of semantic relevance of words based on corpus.And its application in text intelligent processing.Firstly, this paper makes an in-depth investigation on the related techniques of semantic relevance analysis of words in the text, analyzes the current development and application direction of the existing semantic analysis techniques, and compares the advantages and disadvantages of various existing analytical and computational methods.On this basis, this paper completes the key innovation work and main research results, including the following three aspects:1.Based on the complex network model of activation power and the co-occurrence relation of words in context, a method of constructing Dynamic Word Semantic Network is proposed, which is used to analyze the semantic relevance of words in a specific application environment.The experiments show that compared with the existing corpus-based semantic correlation analysis methods, the dynamic word network algorithm has a great improvement both in terms of the accuracy of semantic analysis and the efficiency of the algorithm.2.Based on the above DWSN algorithm, an entity relationship analysis method based on semantic analysis is proposed to mine the latent relationships of named entities in their context.The algorithm has been used in the campus information vertical search engine COSE to mine and display the potential social relationships of school teachers.3.Based on DWSN algorithm, a feature selection transfer learning algorithm based on semantic analysis is proposed.In order to solve the problem of inconsistent feature space between training sample and test sample, the feature of semantic consistency in training sample and test sample is selected as the feature of classification.Experiments show that the proposed algorithm can improve the accuracy of classification by 10% to 20% compared with the traditional classification algorithm.
【學(xué)位授予單位】：北京郵電大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2013
【分類號】：TP391.1

【參考文獻】

相關(guān)期刊論文 前8條

1 張運良;張全;;基于HNC理論的語義相關(guān)度計算方法[J];計算機工程與應(yīng)用;2005年34期

2 王紅玲;呂強;徐瑞;;中文語義相關(guān)度計算模型研究[J];計算機工程與應(yīng)用;2009年07期

3 田萱;李冬梅;;領(lǐng)域本體中概念間語義相關(guān)度的概率估計[J];計算機工程與應(yīng)用;2011年27期

4 劉軍;姚天f ;;基于Wikipedia的語義相關(guān)度計算[J];計算機工程;2010年19期

5 毛小麗;何中市;邢欣來;劉莉;;基于特征選擇的實體關(guān)系抽取[J];計算機應(yīng)用研究;2012年02期

6 徐南軒;鄒恒明;;一種反映詞語相關(guān)度語義庫的構(gòu)建方法[J];上海交通大學(xué)學(xué)報;2008年07期

7 汪祥;賈焰;周斌;丁兆云;梁政;;基于中文維基百科鏈接結(jié)構(gòu)與分類體系的語義相關(guān)度計算[J];小型微型計算機系統(tǒng);2011年11期

8 董振東;語義關(guān)系的表達和知識系統(tǒng)的建造[J];語言文字應(yīng)用;1998年03期

，

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