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WordNet-Wikipedia-Wiktionary: Construction of a Three-way Alignment

机译:WordNet-Wikipedia-Wiktionary:三向对齐的构造

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The coverage and quality of conceptual information contained in lexical semantic resources is crucial for many tasks in natural language processing. Automatic alignment of complementary resources is one way of improving this coverage and quality; however, past attempts have always been between pairs of specific resources. In this paper we establish some set-theoretic conventions for describing concepts and their alignments, and use them to describe a method for automatically constructing n-way alignments from arbitrary pairwise alignments. We apply this technique to the production of a three-way alignment from previously published WordNet-Wikipedia and WordNet-Wiktionary alignments. We then present a quantitative and informal qualitative analysis of the aligned resource. The three-way alignment was found to have greater coverage, an enriched sense representation, and coarser sense granularity than both the original resources and their pairwise alignments, though this came at the cost of accuracy. An evaluation of the induced word sense clusters in a word sense disambiguation task showed that they were no better than random clusters of equivalent granularity. However, use of the alignments to enrich a sense inventory with additional sense glosses did significantly improve the performance of a baseline knowledge-based WSD algorithm.
机译:词汇语义资源中包含的概念信息的覆盖范围和质量对于自然语言处理中的许多任务至关重要。补充资源的自动调整是提高覆盖率和质量的一种方法。但是,过去的尝试始终是在成对的特定资源之间进行的。在本文中,我们建立了一些用于描述概念及其对齐方式的集合理论约定,并使用它们来描述一种从任意成对对齐方式自动构建n向对齐方式的方法。我们将这种技术应用于以前发布的WordNet-Wikipedia和WordNet-Wiktionary对齐方式的三向对齐方式。然后,我们对对齐的资源进行定量和非正式的定性分析。发现三向对齐方式比原始资源及其成对对齐方式具有更大的覆盖范围,更丰富的感知表示以及更粗糙的感知粒度,尽管这是以准确性为代价的。对在词义消歧任务中诱导的词义群集的评估表明,它们并不比等效粒度的随机群集好。但是,使用比对来丰富具有其他感官光泽的感官库存确实可以显着提高基于基线知识的WSD算法的性能。

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