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標題: | 以時間分析與多維度語句呈現為基礎之熱門話題萃取 Hot Topic Extraction with Timeline Analysis and Multidimensional Sentence Modeling |
作者: | Kuan-Yu Chen 陳冠宇 |
指導教授: | 曹承礎 |
關鍵字: | 主題偵測,熱門主題萃取,詞頻與文獻比例頻率,熱門字,多維度句子向量, Topic Detection,Hot Topic Extraction,TF*PDF,Hot Term,Multidimensional Sentence Vector, |
出版年 : | 2005 |
學位: | 碩士 |
摘要: | 主題偵測(Topic Detection)是主題偵測與追蹤(Topic Detection and Tracking)裡其中一個研究領域,該領域試著從新聞媒體裡,進行搜尋、組織及建構文字形式的新聞資料。我們的研究為如何偵測「熱門」的主題(Hot Topic)。所謂熱門的主題,是在某一段時間之內,它會被很多人常常討論與報導。在之前的研究裡,可透過TF*PDF計算文字權重的式子,找到描述熱門主題的「熱門字」 (Hot Term)。不過它仍然會有一些問題存在:(一)只以字的出現頻率和文獻比例頻率為基礎的TF*PDF,萃取熱門字會導致不可靠的結果;(二)只用單一的句子向量並不足以表達句子的涵義。
因此我們提出了改良的熱門主題的萃取系統,來解決上述的兩個問題。首先,我們透過紀錄字在時間上的使用變化,來萃取熱門字;也就是說,追蹤任一個字的生命週期,可以幫助我們來分辨它是否為足以描述「熱門」主題的字。之後,我們使用多維度的句子向量,來描述句子的資訊。最後,我們對所有新聞報導裡的句子進行叢集(cluster),而每一個叢集代表著一個新聞話題。透過以上兩個流程的改善,根據實驗結果顯示,不但增進了每一個叢集的品質,也能夠萃取出一段時間內所包含的熱門主題。 Topic detection is part of the Topic Detection and Tracking field, which seeks to develop technologies that search, organize, and structure news-oriented textual materials from various broadcast news media. We are interested in detecting “hot” topics that are frequently discussed by people in a given period of time. A prior work on hot topic extraction that designed an innovative term-weighting scheme called TF*PDF, which extracts “hot” terms that can describe hot topics. One of the problems that happens in the process of extracting hot topics using TF*PDF is the unreliability of results when the weight is determined solely on term frequency and document frequency. Another problem is that using one single vector misrepresents the meaning of a sentence. We propose a hot topic extraction system that aims to solve the two problems mentioned above. First, we extract the hot terms by capturing their variations of the time distribution within a timeline. In other words, tracking the life cycles of the terms can help us differentiate which term is a real hot term that describes a hot topic. Second, we use multi-dimensional sentence vectors to feature the information of a sentence. Finally we group the sentences of news report into clusters, which represent hot topics. Clustering the sentences by the multi-dimensional sentence vectors not only improves the quality of each cluster, but also extracts most of the actual hot topics over a period of time. |
URI: | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/38414 |
全文授權: | 有償授權 |
顯示於系所單位: | 資訊管理學系 |
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