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Fast paraphrase extraction in Ancient Greek literature

机译:古希腊文学快速解释提取

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In this paper,A shorter version of the paper appeared in German in the final report of the Digital Plato project which was funded by the Volkswagen Foundation from 2016 to 2019. [35], [28]. we present a method for paraphrase extraction in Ancient Greek that can be applied to huge text corpora in interactive humanities applications. Since lexical databases and POS tagging are either unavailable or do not achieve sufficient accuracy for ancient languages, our approach is based on pure word embeddings and the word mover’s distance (WMD) [20]. We show how to adapt the WMD approach to paraphrase searching such that the expensive WMD computation has to be computed for a small fraction of the text segments contained in the corpus, only. Formally, the time complexity will be reduced from O(N·K3·logK)mathcal{O}(Ncdot {K^{3}}cdot log K) to O(N+K3·logK)mathcal{O}(N+{K^{3}}cdot log K), compared to the brute-force approach which computes the WMD between each text segment of the corpus and the search query. N is the length of the corpus and K the size of its vocabulary. The method, which searches not only for paraphrases of the same length as the search query but also for paraphrases of varying lengths, was evaluated on the Thesaurus Linguae Graecae? (TLG?) [25]. The TLG consists of about 75·10675cdot {10^{6}} Greek words. We searched the whole TLG for paraphrases for given passages of Plato. The experimental results show that our method and the brute-force approach, with only very few exceptions, propose the same text passages in the TLG as possible paraphrases. The computation times of our method are in a range that allows its application in interactive systems and let the humanities scholars work productively and smoothly.
机译:在本文中,该文件的较短版本出现在德语中,在Volkswagen基金会从2016年到2019年获得的数字柏拉图项目的最终报告中出现。[35],[28]。我们在古希腊语中提出了一种可用于互动人文应用中的庞大文本语料库的方法。由于词汇数据库和POS标记是不可用的,或者无法为古代语言获得足够的准确性,我们的方法基于纯文字嵌入和移动器距离(WMD)[20]。我们展示了如何使WMD方法调整到释放搜索,使得必须计算昂贵的WMD计算,仅用于Corpus中包含的文本段的小数部分。正式,时间复杂性将减少O.(N.·K.3.·日志K.) mathcal {o}(n cdot {k ^ {3}} cdot log k)到O.(N.+K.3.·日志K.) Mathcal {O}(n + {k ^ {3}} cdot log k),与在语料库的每个文本段之间计算WMD的Brute-Force方法相比。 n是语料库的长度和k的词汇量。该方法不仅针对与搜索查询相同的长度的释义,还针对不同长度的释义,在词库语言中评估了杂志吗? (TLG?)[25]。 TLG由关于75.·10.6.75 cdot {10 ^ {6}}希腊词。我们在整个TLG中寻找帕洛的给定段落。实验结果表明,我们的方法和蛮力方法仅具有很少的例外,提出了TLG中的相同文本段落。我们的方法的计算时间在一个范围内,允许其在交互式系统中的应用,并让人文学者能够高效地工作。

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