利用双熵提取并验证解释性词语群岛
Extracting and Validating Explanatory Word Archipelagoes using Dual Entropy
AI总结：
本文提出利用基于窗口的熵A提取词语群岛，并用基于图的熵B评估文本逻辑连通性，证明无先验知识下提取的解释性文本部分熵更低。
摘要：
文本的逻辑连通性由构成群岛的词语连通性来表示。在这里，每个群岛是某个词语出现所形成的岛屿序列。这里的岛屿指该词语被强调的局部句子序列，长度与目标文本相当的群岛是利用熵A（基于窗口的熵）在词语出现分布上随每个时间窗口宽度的共变来提取的。然后，文本的逻辑连通性通过熵B（基于图的熵）来评估，该熵计算于句子到由词语共现获得的连通词簇的分布上。结果表明，在没有任何已学习或预先准备知识的情况下，利用熵A提取出的形成群岛的词语所对应的目标文本部分，构成了文本中熵B小于基线方法所提取部分的解释性部分。
英文摘要：
The logical connectivity of text is represented by the connectivity of words that form archipelagoes. Here, each archipelago is a sequence of islands of the occurrences of a certain word. An island here means the local sequence of sentences where the word is emphasized, and an archipelago of a length comparable to the target text is extracted using the co-variation of entropy A (the window-based entropy) on the distribution of the word's occurrences with the width of each time window. Then, the logical connectivity of text is evaluated on entropy B (the graph-based entropy) computed on the distribution of sentences to connected word-clusters obtained on the co-occurrence of words. The results show the parts of the target text with words forming archipelagoes extracted on entropy A, without learned or prepared knowledge, form an explanatory part of the text that is of smaller entropy B than the parts extracted by the baseline methods.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
