本文選題：時序推特摘要 + 時序特性��； 參考：《軟件學報》2017年10期

【摘要】：任務(wù)中的一個重要分支,旨在從熱點事件相關(guān)的海量推特流中總結(jié)出隨時間演化的簡要推特集,以幫助用戶快速獲取信息.推特作為當今最流行的社交媒體平臺,其信息量爆發(fā)式的增長以及文本碎片的非結(jié)構(gòu)性,使得單純依賴文本內(nèi)容的傳統(tǒng)摘要方法不再適用.與此同時,社交媒體的新特性也為推特摘要帶來了新的機遇.將推特流視作信號,剖析了其中的復雜噪聲,提出融合推特流隨時序變化的宏微觀信號以及用戶社交上下文語境信息的時序推特摘要新方法.首先,通過小波分析對推特流全局時序信息建模,實現(xiàn)某一關(guān)鍵詞相關(guān)的熱點子事件時間點檢測;接著,融入推特流局部時序信息和用戶社交信息建立推特的隨機步圖模型摘要框架,為每個熱點子事件生成推特摘要.在算法評估過程中,對真實推特數(shù)據(jù)集進行了專家時間點和專家摘要的人工標注,實驗結(jié)果表明了小波分析和融合了時序-社交上下文語境的圖模型在時序推特摘要中的有效性.
[Abstract]:An important branch of the task is to sum up a brief set of tweets that evolve over time from a massive stream of tweets related to hot events to help users quickly obtain information. Twitter is the most popular social media platform nowadays. With the explosive growth of information and the non-structural structure of text fragments, the traditional summary method which relies solely on text content is no longer applicable. At the same time, the new features of social media have opened up new opportunities for Twitter feeds. The Twitter stream is regarded as a signal, the complex noise is analyzed, and a new method of temporal Twitter summary is proposed, which combines the macro and micro signals of the Twitter stream and the context information of the user's social context. Firstly, the global temporal information of Twitter stream is modeled by wavelet analysis to detect the time point of a key word related to a hot sub-event. Based on the local temporal information of Twitter stream and the social information of users, a summary framework of random step graph model of Twitter is established to generate a Twitter summary for each hot sub-event. In the process of algorithm evaluation, the real Twitter data sets are annotated manually with expert time points and expert abstracts. The experimental results show the validity of wavelet analysis and graph model which combines temporal and social context in temporal Twitter summary.
【作者單位】： 天津大學計算機科學與技術(shù)學院;天津市認知計算與應(yīng)用重點實驗室;北京大學信息科學技術(shù)學院;
【基金】：國家重點基礎(chǔ)研究發(fā)展計劃(973)(2013CB329301) 國家自然科學基金(61472277)~~
【分類號】：TP391.1
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本文編號：1838383