SemRe-Rank：通过使用个性化PageRank来描述语义相关性来改进自动术语提取
SemRe-Rank: Improving Automatic Term Extraction By Incorporating Semantic Relatedness With Personalised PageRank
摘要：
术语自动抽取是从特定领域的语料库中抽取术语的过程，长期以来一直是数据和知识获取领域的一个研究领域。ATE仍然是一项具有挑战性的任务，因为众所周知，没有现有的ATE方法可以在任何领域中始终优于其他方法。这项工作采用了刷新的角度来看这个问题：而不是寻找这样一个“一刀切”的解决方案，可能永远不存在，我们建议开发通用的方法来“增强”现有的ATE方法。我们引入SemRe-Rank，基于这一原则的第一种方法，将语义相关性-一个经常被忽视的场地-到现有的ATE方法，以进一步提高其性能。SemRe-Rank将词嵌入结合到个性化的PageRank过程中，以从语义相关词（节点）的图形中计算候选词的“语义重要性”分数，然后用于修改由基本ATE算法计算的候选词的分数。在四个不同性质的数据集上使用13种最先进的基础ATE方法进行了广泛评估，结果表明，在所有基础方法和所有数据集上都取得了广泛的改进，当通过排名最高的K个候选术语的精度测量时，（一组K的平均值），或者在F1中高达28个百分点，以等于候选人预期实际条件的K（简称F1）测量。与基于著名的TextRank算法的替代方法相比，SemRe-Rank在前K时的精度可能会高出8个点，在F1中可能会高出17个点。
英文摘要：
Automatic Term Extraction deals with the extraction of terminology from a domain specific corpus, and has long been an established research area in data and knowledge acquisition. ATE remains a challenging task as it is known that there is no existing ATE methods that can consistently outperform others in any domain. This work adopts a refreshed perspective to this problem: instead of searching for such a 'one-size-fit-all' solution that may never exist, we propose to develop generic methods to 'enhance' existing ATE methods. We introduce SemRe-Rank, the first method based on this principle, to incorporate semantic relatedness - an often overlooked venue - into an existing ATE method to further improve its performance. SemRe-Rank incorporates word embeddings into a personalised PageRank process to compute 'semantic importance' scores for candidate terms from a graph of semantically related words (nodes), which are then used to revise the scores of candidate terms computed by a base ATE algorithm. Extensively evaluated with 13 state-of-the-art base ATE methods on four datasets of diverse nature, it is shown to have achieved widespread improvement over all base methods and across all datasets, with up to 15 percentage points when measured by the Precision in the top ranked K candidate terms (the average for a set of K's), or up to 28 percentage points in F1 measured at a K that equals to the expected real terms in the candidates (F1 in short). Compared to an alternative approach built on the well-known TextRank algorithm, SemRe-Rank can potentially outperform by up to 8 points in Precision at top K, or up to 17 points in F1.
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。
