基于微博特定實體的關(guān)聯(lián)信息挖掘算法研究
發(fā)布時間：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�。微博的火爆帶來的是微博�?shù)據(jù)量的爆炸式增長。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,如何挖掘和指定實體的關(guān)聯(lián)信息,成為現(xiàn)階段學(xué)術(shù)界的重點研究方向。 本論文通過分析微博的特點,提出了基于微博的特定實體對象的信息挖掘系統(tǒng)——微郵系統(tǒng),并從微博環(huán)境下的信息檢索,特定實體信息挖掘和基于實體間關(guān)聯(lián)性的推薦系統(tǒng)三個方面由淺入深地進行了研究。本文的主要創(chuàng)新點和貢獻在于以下幾個方面： 首先,提出了一種基于電阻網(wǎng)絡(luò)模型的查詢擴展方法,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。此方法有效地簡化了復(fù)雜的詞間關(guān)系網(wǎng)絡(luò)的計算。TREC提出的Microblog Track評測的結(jié)果表明,此方法可以得到符合用戶原始查詢意圖的擴展詞,并提高各項檢索指標。 其次,在查詢擴展的基礎(chǔ)上,提出了一種基于詞激活力模型的擴展詞間關(guān)聯(lián)性挖掘算法。利用詞激活力模型中詞間親密度,計算擴展詞問的關(guān)聯(lián)性,得到擴展詞對,并利用擴展詞對進行查詢重構(gòu)。實驗數(shù)據(jù)說明,擴展詞對可以有效減少因擴展詞引起的信息偏移,在關(guān)于實體對象的信息挖掘中取得了較好的效果。 最后,設(shè)計實現(xiàn)了一個基于詞激活力模型,針對用戶興趣和環(huán)境信息共同影響下的個性化推薦系統(tǒng)。此系統(tǒng)在TREC的Contextual Suggestion Track評測中取得了優(yōu)異的成果,充分說明了詞激活力模型在實體間關(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)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：TP393.092;TP391.1
本文編號：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)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：TP393.092;TP391.1
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