發(fā)布時(shí)間：2018-07-28 07:39

【摘要】：微博迅速成為一種重要的實(shí)時(shí)信息源,其搜索存在兩個(gè)重要問題：查詢?cè)~與微博消息的相關(guān)性計(jì)算、搜索結(jié)果整理。相關(guān)性計(jì)算從內(nèi)容、語義上衡量消息與查詢?cè)~之間的相似程度；搜索結(jié)果整理以簡(jiǎn)明有序的方式組織消息,克服冗余性和不規(guī)范書寫,主要整理方式包括分類、摘要等。本文以推特為例,探索了微博搜索研究任務(wù)中幾個(gè)重要問題：相關(guān)性計(jì)算、查詢結(jié)果分類、摘要和對(duì)比話題摘要。 針對(duì)相關(guān)性計(jì)算問題,提出兩種消息排序模型,分別基于學(xué)習(xí)排序和遞歸神經(jīng)網(wǎng)絡(luò)語言模型。與目前微博搜索服務(wù)中的相關(guān)性排序算法比較,前者顯著提高了消息序列的相關(guān)性,后者縮短消息在計(jì)算語義相關(guān)度時(shí)的鴻溝,提高查詢結(jié)果的覆蓋率�；趯W(xué)習(xí)排序的模型系統(tǒng)研究了文本相關(guān)性特征、微博書寫特征和微博的作者權(quán)威度特征在微博相關(guān)性計(jì)算中的作用�；谶f歸神經(jīng)網(wǎng)絡(luò)語言模型的排序模型將語義相似度引入消息的相關(guān)性計(jì)算中,在詞向量粒度上計(jì)算消息之間的詞法語義相似度。 針對(duì)搜索結(jié)果分類問題,提出基于消息關(guān)聯(lián)關(guān)系的協(xié)同分類模型,為微博定義了一個(gè)話題分類體系。與基于特征的基準(zhǔn)模型相比,該模型的準(zhǔn)確率和F值分別提高了5.38%和4.74%。該模型將消息之間的兩種共享話題關(guān)系應(yīng)用到三種基于圖的協(xié)同分類模型中,考慮本地特征和來自關(guān)聯(lián)消息的類別分布,同時(shí)為一批微博消息分類,降低數(shù)據(jù)稀疏的影響,極大地提高了分類器的精確率和召回率,其中采用共享話題標(biāo)簽(#hashtag)關(guān)系的迭代分類算法結(jié)果最優(yōu)。 針對(duì)搜索結(jié)果摘要問題,提出基于時(shí)間軸的關(guān)聯(lián)交互自增強(qiáng)式摘要模型。與基于圖的基準(zhǔn)模型比較,該模型的ROUGE-1平均提高了14%。給定查詢?cè)~的搜索結(jié)果,該模型按照時(shí)間順序?qū)⑵鋭澐殖扇舾勺釉掝},同時(shí)考慮文本內(nèi)容、作者社會(huì)影響力和文本質(zhì)量計(jì)算消息的重要度,根據(jù)重要度和多樣性對(duì)微博消息進(jìn)行排序和抽取以生成摘要。實(shí)驗(yàn)表明,作者的社會(huì)影響力和文本質(zhì)量有效地改進(jìn)了文本重要度的度量。 針對(duì)對(duì)比話題摘要問題,提出基于消息關(guān)聯(lián)關(guān)系的最優(yōu)化對(duì)比話題摘要模型。與基于內(nèi)容相似度計(jì)算的基準(zhǔn)模型比較,該模型的對(duì)比屬性覆蓋率和比較消息對(duì)準(zhǔn)確率分別提高了14.7%和11.6%。該模型充分利用消息之間的相似度關(guān)系和三種共享話題關(guān)系,采用網(wǎng)頁排序算法和SimRank方法,最大化消息對(duì)的內(nèi)部對(duì)比性和話題代表性,概括對(duì)比查詢?cè)~搜索結(jié)果中的共同點(diǎn)和不同點(diǎn)生成摘要。
[Abstract]:Weibo has quickly become an important source of real-time information. There are two important problems in its search: the correlation calculation between query words and Weibo messages, and the sorting of search results. Correlation calculation measures the similarity between messages and query words in terms of content and semantics. Search results organize messages in a concise and orderly manner to overcome redundancy and non-standard writing. The main sorting methods include classification, summary and so on. Taking Twitter as an example, this paper explores several important issues in Weibo search research tasks: correlation calculation, query result classification, summary and comparative topic summary. In order to solve the problem of correlation computation, two message ordering models are proposed, which are based on learning sorting and recursive neural network language model. Compared with the existing correlation sorting algorithm in Weibo search service, the former significantly improves the correlation of message sequences, while the latter shortens the gap in the calculation of semantic correlation and improves the coverage of query results. The model based on learning ranking systematically studies the role of text correlation features, Weibo writing features and authorship features of Weibo in the calculation of Weibo correlation. The ranking model based on recursive neural network language model introduces semantic similarity into message correlation calculation and calculates lexical semantic similarity between messages on word vector granularity. To solve the problem of search result classification, a cooperative classification model based on message association is proposed, and a topic classification system is defined for Weibo. Compared with the feature-based benchmark model, the accuracy and F value of the model are increased by 5.38% and 4.74%, respectively. The model applies two kinds of shared topic relationships between messages to three graph-based cooperative classification models, considering local features and category distribution from associated messages. At the same time, it classifies a batch of Weibo messages to reduce the effect of data sparsity. The precision and recall rate of the classifier are greatly improved, and the iterative classification algorithm using #hashtag relation is optimal. In order to solve the problem of search result summary, a time axis based autoenhancement model of associative interaction is proposed. Compared with the graph-based benchmark model, the average ROUGE-1 of this model is increased by 14%. Given the search results of the query words, the model divides the query words into several sub-topics according to the time order, and considers the content of the text, the author's social influence and the importance of calculating the message of the text quality. Weibo messages are sorted and extracted according to importance and diversity to generate abstracts. Experiments show that the author's social influence and text quality can effectively improve the measurement of text importance. Aiming at the problem of comparative topic summary, an optimal model of contrast topic summary based on message association relationship is proposed. Compared with the benchmark model based on content similarity calculation, the comparison attribute coverage and comparison message pair accuracy of the model are improved by 14.7% and 11.6%, respectively. The model makes full use of the similarity relationship between messages and three kinds of shared topic relationships, and uses web page sorting algorithm and SimRank method to maximize the internal comparison and topic representation of message pairs. Generalize and compare the common points and differences in the search results of query terms to generate a summary.
【學(xué)位授予單位】：中國科學(xué)技術(shù)大學(xué)
【學(xué)位級(jí)別】：博士
【學(xué)位授予年份】：2014
【分類號(hào)】：TP393.092;TP391.1

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