基于微博特定實(shí)體的關(guān)聯(lián)信息挖掘算法研究
發(fā)布時(shí)間：2019-04-28 17:42
【摘要】：作為隨著web2.0技術(shù)而興起的互聯(lián)網(wǎng)社交類應(yīng)用,微博已經(jīng)逐漸成為人們?nèi)粘Ｉ罾锊豢苫蛉钡囊徊糠帧Ｎ⒉┑幕鸨瑤淼氖俏⒉⿺?shù)據(jù)量的爆炸式增長(zhǎng)。如何利用龐大的微博數(shù)據(jù),如何從海量數(shù)據(jù)中獲得符合需求的信息,如何挖掘和指定實(shí)體的關(guān)聯(lián)信息,成為現(xiàn)階段學(xué)術(shù)界的重點(diǎn)研究方向。 本論文通過分析微博的特點(diǎn),提出了基于微博的特定實(shí)體對(duì)象的信息挖掘系統(tǒng)——微郵系統(tǒng),并從微博環(huán)境下的信息檢索,特定實(shí)體信息挖掘和基于實(shí)體間關(guān)聯(lián)性的推薦系統(tǒng)三個(gè)方面由淺入深地進(jìn)行了研究。本文的主要?jiǎng)?chuàng)新點(diǎn)和貢獻(xiàn)在于以下幾個(gè)方面： 首先,提出了一種基于電阻網(wǎng)絡(luò)模型的查詢擴(kuò)展方法,利用電路系統(tǒng)上的電阻網(wǎng)絡(luò)模型來模擬文本空間的詞間關(guān)系網(wǎng)絡(luò),以有效電阻來表征詞間的關(guān)聯(lián)度。此方法有效地簡(jiǎn)化了復(fù)雜的詞間關(guān)系網(wǎng)絡(luò)的計(jì)算。TREC提出的Microblog Track評(píng)測(cè)的結(jié)果表明,此方法可以得到符合用戶原始查詢意圖的擴(kuò)展詞,并提高各項(xiàng)檢索指標(biāo)。 其次,在查詢擴(kuò)展的基礎(chǔ)上,提出了一種基于詞激活力模型的擴(kuò)展詞間關(guān)聯(lián)性挖掘算法。利用詞激活力模型中詞間親密度,計(jì)算擴(kuò)展詞問的關(guān)聯(lián)性,得到擴(kuò)展詞對(duì),并利用擴(kuò)展詞對(duì)進(jìn)行查詢重構(gòu)。實(shí)驗(yàn)數(shù)據(jù)說明,擴(kuò)展詞對(duì)可以有效減少因擴(kuò)展詞引起的信息偏移,在關(guān)于實(shí)體對(duì)象的信息挖掘中取得了較好的效果。 最后,設(shè)計(jì)實(shí)現(xiàn)了一個(gè)基于詞激活力模型,針對(duì)用戶興趣和環(huán)境信息共同影響下的個(gè)性化推薦系統(tǒng)。此系統(tǒng)在TREC的Contextual Suggestion Track評(píng)測(cè)中取得了優(yōu)異的成果,充分說明了詞激活力模型在實(shí)體間關(guān)聯(lián)性挖掘上的有效性。
[Abstract]:With the rise of web2.0 technology, Internet social applications, Weibo has gradually become an indispensable part of people's daily life. The explosion of Weibo results in an explosive increase in the amount of Weibo data. How to make use of huge Weibo data, how to obtain the required information from the massive data, how to mine and identify the associated information of entities, has become the focus of academic research at this stage. By analyzing the characteristics of Weibo, this paper puts forward the information mining system of specific entity object based on Weibo-micro-mail system, and retrieves the information from Weibo environment. Specific entity information mining and recommendation system based on inter-entity association are studied from shallow to deep. The main innovations and contributions of this paper lie in the following aspects: firstly, a query extension method based on resistance network model is proposed, which uses the resistance network model on the circuit system to simulate the inter-word relation network in text space. Use effective resistance to characterize the correlation between words. This method effectively simplifies the computation of complex word-to-word relationship networks. The results of Microblog Track evaluation proposed by TREC show that this method can obtain extended words that accord with the original query intention of users and improve the retrieval indexes. Secondly, on the basis of query extension, an extended word-to-word association mining algorithm based on word vitality model is proposed. By using the affinity density between words in the dynamic model of words, the relevance of extended word questions is calculated, the extended word pairs are obtained, and the extended word pairs are used for query reconstruction. The experimental data show that the extended word pair can effectively reduce the information offset caused by the extended word and obtain a good effect in the information mining of the entity object. Finally, a personalized recommendation system based on word activation model is designed and implemented, which is influenced by user's interest and environmental information. This system has achieved excellent results in the Contextual Suggestion Track evaluation of TREC, which fully demonstrates the validity of the word activation model in the mining of association between entities.
【學(xué)位授予單位】：北京郵電大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：TP393.092;TP391.1
本文編號(hào)：2467832
[Abstract]:With the rise of web2.0 technology, Internet social applications, Weibo has gradually become an indispensable part of people's daily life. The explosion of Weibo results in an explosive increase in the amount of Weibo data. How to make use of huge Weibo data, how to obtain the required information from the massive data, how to mine and identify the associated information of entities, has become the focus of academic research at this stage. By analyzing the characteristics of Weibo, this paper puts forward the information mining system of specific entity object based on Weibo-micro-mail system, and retrieves the information from Weibo environment. Specific entity information mining and recommendation system based on inter-entity association are studied from shallow to deep. The main innovations and contributions of this paper lie in the following aspects: firstly, a query extension method based on resistance network model is proposed, which uses the resistance network model on the circuit system to simulate the inter-word relation network in text space. Use effective resistance to characterize the correlation between words. This method effectively simplifies the computation of complex word-to-word relationship networks. The results of Microblog Track evaluation proposed by TREC show that this method can obtain extended words that accord with the original query intention of users and improve the retrieval indexes. Secondly, on the basis of query extension, an extended word-to-word association mining algorithm based on word vitality model is proposed. By using the affinity density between words in the dynamic model of words, the relevance of extended word questions is calculated, the extended word pairs are obtained, and the extended word pairs are used for query reconstruction. The experimental data show that the extended word pair can effectively reduce the information offset caused by the extended word and obtain a good effect in the information mining of the entity object. Finally, a personalized recommendation system based on word activation model is designed and implemented, which is influenced by user's interest and environmental information. This system has achieved excellent results in the Contextual Suggestion Track evaluation of TREC, which fully demonstrates the validity of the word activation model in the mining of association between entities.
【學(xué)位授予單位】：北京郵電大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：TP393.092;TP391.1
【參考文獻(xiàn)】
相關(guān)期刊論文 前9條
1 董振東,董強(qiáng);知網(wǎng)和漢語研究[J];當(dāng)代語言學(xué);2001年01期
2 馬暉男;吳江寧;潘東華;;一種基于同義詞詞典的模糊查詢擴(kuò)展方法[J];大連理工大學(xué)學(xué)報(bào);2007年03期
3 魏曉寧;;基于隱馬爾科夫模型的中文分詞研究[J];電腦知識(shí)與技術(shù)(學(xué)術(shù)交流);2007年21期
4 韓立新,陳貴海,謝立;一個(gè)面向Internet的個(gè)性化信息檢索系統(tǒng)模型[J];電子學(xué)報(bào);2002年02期
5 高茂庭;王正歐;;一種基于雙詞關(guān)聯(lián)的文本特征選擇模型[J];計(jì)算機(jī)工程與應(yīng)用;2007年10期
6 鄒海山,吳勇,吳月珠,陳陣;中文搜索引擎中的中文信息處理技術(shù)[J];計(jì)算機(jī)應(yīng)用研究;2000年12期
7 董振東;董強(qiáng);郝長(zhǎng)伶;;知網(wǎng)的理論發(fā)現(xiàn)[J];中文信息學(xué)報(bào);2007年04期
8 劉海峰;王元元;張學(xué)仁;劉守生;;一種基于聚類和LSA相結(jié)合的文本特征降維方法[J];情報(bào)雜志;2008年02期
9 丁立愷;夏勇明;錢松榮;;基于詞關(guān)聯(lián)度的文本檢索系統(tǒng)[J];微型電腦應(yīng)用;2011年03期
，本文編號(hào)：2467832
本文鏈接：http://www.wukwdryxk.cn/guanlilunwen/ydhl/2467832.html
最近更新
教材專著
