Abstract This paper discusses a variety of potential shortcomings of most of the most widely-used association measures as used in collocation research and collostructional analyses. To address these shortcomings, I then discuss a research program called tupleization, an approach that does away with the usual kinds of information conflation by keeping relevant corpus-linguistic dimensions of information – e.g. frequency, association/contingency, dispersion, entropy, etc. – separate and analyzing them in a multidimensional way; I conclude with pointers towards how these dimensions could, if deemed absolutely necessary, be conflated for the simplest kinds of of rankings as well as strategies for future research.

中文翻译：

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15年的搭配

摘要 本文讨论了搭配研究和搭配分析中使用的大多数最广泛使用的关联度量的各种潜在缺点。为了解决这些缺点，我然后讨论了一个称为元组化的研究程序，这种方法通过保留信息的相关语料库语言维度（例如频率、关联/偶然性、分散、熵等）来消除通常类型的信息混淆。以多维的方式分离和分析它们；我最后指出，如果认为绝对必要，如何将这些维度合并为最简单的排名类型以及未来研究的策略。