We review lexical Association Measures (AMs) that have been employed by past work in extracting multiword expressions. Our work contributes to the understanding of these AMs by categorizing them into two groups and suggesting the use of rank equivalence to group AMs with the same ranking performance. We also examine how existing AMs can be adapted to better rank English verb particle constructions and light verb constructions. Specifically, we suggest normalizing (Pointwise) Mutual Information and using marginal frequencies to construct penalization terms. We empirically validate the effectiveness of these modified AMs in detection tasks in English, performed on the Penn Treebank, which shows significant improvement over the original AMs.