本文選題：主題模型 + 文本挖掘　； 參考：《復雜系統與復雜性科學》2017年01期

【摘要】：Labeled LDA能挖掘出給定主題下的單詞概率分布,但卻無法分析主題詞之間的關聯關系。采用PMI雖可計算兩個單詞的相互關系,但卻和給定主題失去聯系。受PMI在窗口中統計詞對共現頻率的啟發(fā),提出了一種PL-LDA(Pointwise Labeled LDA)主題模型,可計算給定主題下詞對的聯合概率分布,在航空安全報告數據集上的實驗表明PL-LDA模型所得結果具有很好的解釋性。利用PL-LDA構建了主題文本網絡,該網絡除能反映主題詞分布外,還可展現它們之間的復雜關聯關系。
[Abstract]:Labeled LDA can mine the probability distribution of words under a given topic, but it can not analyze the relationship between the subject words. The PMI can be used to calculate the relationship between two words, but it is not related to a given subject. A PL-LDA-Pointwise Labeled LDA-topic model is proposed to calculate the joint probability distribution of word pairs under a given topic. Experiments on the data set of aviation safety report show that the results of PL-LDA model are well explained. PL-LDA is used to construct a topic text network, which can not only reflect the distribution of theme words, but also show the complex relationship between them.
【作者單位】： 中國民航大學計算機科學與技術學院;南京航空航天大學計算機科學與技術學院;
【基金】：國家自然科學基金(61201414,61301245,U1233113)
【分類號】：TP391.1
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本文編號：2033531