本文關(guān)鍵詞： 事件語義 語義聚集融合 語義激活擴(kuò)散 關(guān)聯(lián)語義鏈網(wǎng)絡(luò) 人類聯(lián)想記憶　出處：《上海大學(xué)》2016年博士論文　論文類型：學(xué)位論文

【摘要】：近些年來,以新聞報(bào)道、博客信息、論壇熱帖為代表的輿情文本出現(xiàn)了井噴式的發(fā)展。面對這些規(guī)模海量、增量快速、分布松散、關(guān)聯(lián)稀疏的輿情文本,用戶希望能夠從其中發(fā)現(xiàn)事件的核心語義信息,以幫助他們及時(shí)全面地了解事件發(fā)展與演化的進(jìn)程;同時(shí),面對實(shí)時(shí)發(fā)生、動(dòng)態(tài)演化的事件語義信息,用戶也希望能夠從中發(fā)現(xiàn)其所關(guān)心的事件知識,以獲得個(gè)性化的事件知識服務(wù)。本文面向輿情文本處理的具體需求,提出事件語義聚集融合與激活擴(kuò)散方法及其理論,并對相關(guān)應(yīng)用展開研究。具體內(nèi)容包括:1.以關(guān)聯(lián)語義鏈網(wǎng)絡(luò)為基礎(chǔ),本文提出事件語義聚集融合方法,包括:首先,通過事件語義社區(qū)發(fā)現(xiàn)方法,從輿情文本中進(jìn)行事件語義的聚集;然后,通過文本映射至事件語義社區(qū)以及事件語義社區(qū)重構(gòu),實(shí)現(xiàn)對聚集事件語義的融合,豐富聚集的事件語義。最終,通過關(guān)聯(lián)語義鏈網(wǎng)絡(luò)的分裂迭代,不斷對事件語義進(jìn)行聚集和融合,以發(fā)現(xiàn)事件語義,幫助用戶了解實(shí)時(shí)動(dòng)態(tài)的事件語義信息。2.通過模擬人類聯(lián)想記憶激活擴(kuò)散模型的語義提取過程,本文提出人類聯(lián)想記憶的語義激活擴(kuò)散過程,對用戶需求相關(guān)語義進(jìn)行擴(kuò)展。在此基礎(chǔ)上,本文提出事件語義激活擴(kuò)散方法,針對用戶事件語義需求,通過語義激活擴(kuò)散過程,發(fā)現(xiàn)事件骨干詞匯、事件知識流和事件語義社區(qū)三種粒度事件知識,提供個(gè)性化且語義豐富的事件知識服務(wù)。3.本文將事件語義聚集融合方法應(yīng)用在事件發(fā)現(xiàn)中,提出事件發(fā)現(xiàn)算法。首先,提取輿情文本流中文本的語義特征;然后,從中發(fā)現(xiàn)已有事件的后續(xù)報(bào)道,進(jìn)行已有事件的跟蹤;進(jìn)而,依據(jù)事件語義聚集融合方法發(fā)現(xiàn)新發(fā)生事件�；诹鶄�(gè)事件數(shù)據(jù)集和五種事件發(fā)現(xiàn)評價(jià)指標(biāo),進(jìn)行事件發(fā)現(xiàn)準(zhǔn)確率實(shí)驗(yàn)和對比實(shí)驗(yàn),以及在線的事件發(fā)現(xiàn)場景下的準(zhǔn)確率實(shí)驗(yàn)和對比實(shí)驗(yàn),并進(jìn)行性能分析,驗(yàn)證我們提出的事件發(fā)現(xiàn)算法的準(zhǔn)確性和有效性。4.本文將事件語義激活擴(kuò)散方法應(yīng)用在事件知識推薦中,提出事件知識推薦算法。針對用戶知識需求,通過事件語義激活擴(kuò)散方法,向用戶推薦多粒度事件知識,并提取相關(guān)文本作為事件知識背景,幫助用戶加深對事件知識的理解;同時(shí),感知用戶興趣變化,對用戶興趣偏好進(jìn)行增強(qiáng)和抑制,使得后續(xù)推薦更加符合用戶個(gè)性化需求�；诹鶄�(gè)事件數(shù)據(jù)集和四種推薦評價(jià)指標(biāo),進(jìn)行事件知識推薦對比實(shí)驗(yàn)、事件知識交互推薦實(shí)驗(yàn),并對事件知識推薦案例和算法性能進(jìn)行分析,驗(yàn)證事件知識推薦算法的準(zhǔn)確性和有效性。本文的研究內(nèi)容為面向文本的事件語義研究及其應(yīng)用提供了理論支持和具體方法,可應(yīng)用于面向文本的事件語義組織和表示、事件的語義標(biāo)注、事件檢測與跟蹤、事件語義搜索、事件知識提取,事件語義推薦等方面,在一定程度上解決Web事件信息爆炸而知識匱乏的問題。
[Abstract]:In recent years, public opinion texts, represented by news reports, blog information and forum hot posts, have developed in a blowout manner. In the face of these massive, fast incremental, loosely distributed, sparse public opinion texts, Users hope to discover the core semantic information of events in order to help them understand the development and evolution of events in a timely and comprehensive manner, and at the same time, in the face of real-time, dynamic evolution of event semantic information, Users also hope to find the event knowledge they are concerned about, so as to obtain personalized event knowledge service. In this paper, the method of event semantic aggregation fusion and activation diffusion and its theory are proposed to meet the specific needs of public opinion text processing. The specific contents include: 1. Based on the associative semantic chain network, this paper proposes a fusion method of event semantic aggregation, including: first, through the event semantic community discovery method, The aggregation of event semantics is carried out from the text of public opinion, and then, through the mapping of the text to the community of event semantics and the reconstruction of the semantic community of events, the fusion of the semantics of aggregation events is realized, and the event semantics of aggregation is enriched. Through the split iteration of the association semantic chain network, the event semantics are continuously aggregated and fused to discover the event semantics. By simulating the semantic extraction process of human associative memory activation diffusion model, this paper proposes the semantic activation diffusion process of human associative memory. On the basis of this, this paper proposes a method of event semantic activation diffusion, which aims at user event semantic requirements, through semantic activation diffusion process, the event backbone vocabulary is found. Event knowledge flow and event semantic community provide personalized and semantic event knowledge service. 3. In this paper, event semantic aggregation fusion method is applied to event discovery, and an event discovery algorithm is proposed. Extract the semantic features of the Chinese text of the public opinion text stream; then, find out the subsequent reports of the existing events, and track the existing events; then, Based on six event data sets and five evaluation indexes of event discovery, the accuracy of event discovery is tested and compared. As well as online event discovery scene accuracy experiments and contrast experiments, and performance analysis, Verify the accuracy and validity of the event discovery algorithm. 4. This paper applies the event semantic activation diffusion method to the event knowledge recommendation, and proposes the event knowledge recommendation algorithm. Through the method of event semantic activation diffusion, the multi-granularity event knowledge is recommended to the user, and the relevant text is extracted as the background of event knowledge, which helps the user to deepen his understanding of the event knowledge, at the same time, the user is aware of the change of user interest. Based on six event data sets and four recommended evaluation indexes, the comparison experiment of event knowledge recommendation and event knowledge interactive recommendation experiment is carried out. The performance of event knowledge recommendation cases and algorithms is analyzed to verify the accuracy and effectiveness of event knowledge recommendation algorithms. The research content of this paper provides theoretical support and specific methods for text-oriented event semantics research and its application. It can be used in text-oriented event semantic organization and representation, event semantic annotation, event detection and tracking, event semantic search, event knowledge extraction, event semantic recommendation, etc. To some extent, solve the problem of Web event information explosion and lack of knowledge.
【學(xué)位授予單位】：上海大學(xué)
【學(xué)位級別】：博士
【學(xué)位授予年份】：2016
【分類號】：TP391.1

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本文編號：1507765