本文選題：概念圖 + 概念關(guān)系抽取��； 參考：《西北大學(xué)》2013年碩士論文

【摘要】：動漫產(chǎn)業(yè)的迅速發(fā)展帶動了經(jīng)濟(jì)增長并豐富了人們的生活,而傳統(tǒng)的動畫生成需要大量人工繁瑣的勞動,動畫的自動生成可有效的將人力解放出來,節(jié)省大量資源。動畫劇本的生成是動畫自動生成中的關(guān)鍵環(huán)節(jié),是連接故事文本與動畫場面的橋梁,如何抽取動畫劇本所需的劇本元素,并選取合適的方式將劇本組織起來是解決問題的關(guān)鍵。 對于動畫劇本生成的研究,國外已有相當(dāng)數(shù)量的研究成果,而由于中文構(gòu)詞的特殊性,國內(nèi)的研究還有所欠缺。本文運(yùn)用了自然語言理解技術(shù),將劇本生成系統(tǒng)分成了三個部分：故事理解、概念關(guān)系抽取、劇本其它元素的抽取。具體內(nèi)容如下： 在故事理解中,本文選擇了概念圖這一知識表達(dá)工具對故事文本做出形式化描述。因?yàn)橹R表達(dá)是本系統(tǒng)的基礎(chǔ),而概念圖這種與自然語言互譯的知識表示工具能夠很好的進(jìn)行語義分析和推理。本文改進(jìn)了概念圖的生成算法,提出了一種主線圖的方法。主線圖是在概念圖生成的基礎(chǔ)上,通過概念圖的運(yùn)算,得到包含主要角色和主要情節(jié)的圖。通過主線圖這一工具,獲得對故事的總體理解和把握。 概念關(guān)系的抽取主要是抽取童話故事中角色與場景、角色與角色以及角色與道具之間的關(guān)系。本文改進(jìn)了傳統(tǒng)的模板匹配方法,結(jié)合概念圖的強(qiáng)大語義表達(dá)功能,通過總結(jié)常見關(guān)系和規(guī)則映射得到概念圖模板庫,進(jìn)而利用概念圖匹配運(yùn)算來抽取相應(yīng)關(guān)系。實(shí)驗(yàn)表明,采用基于概念圖的方法,概念關(guān)系抽取的準(zhǔn)確率和召回率得到有效的提高。 劇本元素除了概念關(guān)系之外,還有角色名、道具名以及場景信息等。在此首先解決了未登錄詞的識別問題,提出了基于知網(wǎng)的混合識別方法進(jìn)行識別角色與道具。由于在識別中增加了語義因素,這種方法識別的效果要優(yōu)于傳統(tǒng)的基于規(guī)則和統(tǒng)計(jì)的方法。針對場景信息中的時(shí)間地點(diǎn)等信息,通過建立規(guī)則庫,采用基于規(guī)則的方式進(jìn)行有效抽取。最后選取動畫劇本標(biāo)記語言CSML(cartoon scenario markup language)對抽取的信息進(jìn)行描述,形成動畫劇本。
[Abstract]:The rapid development of animation industry has led to economic growth and enriched people's lives, but the traditional animation production needs a lot of labor, animation automatic generation can effectively liberate human resources and save a lot of resources. Animation script generation is a key link in automatic animation generation, it is a bridge between the story text and the animation scene. How to extract the script elements needed for the animation script and choose the appropriate way to organize the script is the key to solve the problem. For the animation script generation research, there have been a considerable number of foreign research results, but due to the particularity of Chinese word-formation, there is still a lack of domestic research. This paper uses natural language understanding technology to divide the script generation system into three parts: story understanding, concept relation extraction, script other elements extraction. The details are as follows: In the process of story understanding, this paper chooses concept graph as a knowledge representation tool to formalize the story text. Because knowledge representation is the foundation of this system, concept map, which is a knowledge representation tool with natural language translation, can do semantic analysis and reasoning well. In this paper, the algorithm of generating concept graph is improved, and a method of principal graph is proposed. The main line graph is based on the generation of the concept graph, and through the operation of the concept graph, the graph containing the main characters and the main plot is obtained. Through the main line map this tool, obtains the overall understanding and the grasp to the story. The extraction of conceptual relations is mainly to extract the relationship between characters and scenes, roles and characters, and between characters and props in fairy tales. This paper improves the traditional template matching method, combines the powerful semantic expression function of the concept map, and obtains the concept map template library by summarizing the common relation and the rule mapping, and then extracts the corresponding relation by using the concept map matching operation. Experiments show that the accuracy and recall rate of concept relation extraction are improved effectively by using concept graph based method. In addition to conceptual relationships, script elements include role names, props names and scene information. In this paper, the problem of recognition of unrecorded words is first solved, and a hybrid recognition method based on knowledge net is proposed to identify characters and props. Due to the addition of semantic factors in recognition, this method is superior to the traditional rule-based and statistical methods. Aiming at the information of time and place in scene information, the rule base is established and the rule based approach is used to extract the information effectively. Finally, the animation script markup language CSML(cartoon scenario markup language) is selected to describe the extracted information to form the animation script.
【學(xué)位授予單位】：西北大學(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(xiàn)】

相關(guān)期刊論文 前10條

1 蔡勇智;;未登錄詞識別算法的改進(jìn)[J];福建電腦;2006年03期

2 陳紅琳;;自然語言的形式化[J];阜陽師范學(xué)院學(xué)報(bào)(社會科學(xué)版);2009年02期

3 尹蘭;王家瑋;;自然語言語義形式化二維視角[J];福建電腦;2012年05期

4 劉曉霞;新的知識表示方法-概念圖[J];航空計(jì)算技術(shù);1997年04期

5 陳耀東;王挺;陳火旺;;淺層語義分析研究[J];計(jì)算機(jī)研究與發(fā)展;2008年S1期

6 劉巖;張蕾;;基于概念圖的中文信息自動轉(zhuǎn)化技術(shù)研究[J];計(jì)算機(jī)工程與應(yīng)用;2008年15期

7 白振興;;一種新的知識表達(dá)方法:概念結(jié)構(gòu)[J];計(jì)算機(jī)科學(xué);1992年06期

8 江瀾;岳小莉;馮柏嵐;李良軍;曹存根;;基于實(shí)例和約束的三維場景概念設(shè)計(jì)和系統(tǒng)實(shí)現(xiàn)[J];計(jì)算機(jī)科學(xué);2008年01期

9 周蕾;朱巧明;;基于統(tǒng)計(jì)和規(guī)則的未登錄詞識別方法研究[J];計(jì)算機(jī)工程;2007年08期

10 王玨，袁小紅，，石純一，郝繼剛;關(guān)于知識表示的討論[J];計(jì)算機(jī)學(xué)報(bào);1995年03期

相關(guān)博士學(xué)位論文 前1條

1 張蕾;概念結(jié)構(gòu)及其應(yīng)用[D];西北工業(yè)大學(xué);2001年

相關(guān)碩士學(xué)位論文 前4條

1 劉龍;音樂領(lǐng)域全局實(shí)體關(guān)系抽取研究[D];哈爾濱工業(yè)大學(xué);2010年

2 馮禮;基于事件框架的突發(fā)事件信息抽取[D];上海交通大學(xué);2008年

3 李紅良;火箭發(fā)射環(huán)境信息的推理技術(shù)應(yīng)用研究[D];重慶大學(xué);2009年

4 周文;基于XML的動畫劇本描述及其應(yīng)用研究[D];國防科學(xué)技術(shù)大學(xué);2010年

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