A Critique of a Critique of Word Similarity Datasets: Sanity Check or Unnecessary Confusion?

Batchkarov et al. (2016) is one of evaluation/methodology papers much needed in NLP and I hope we’ll have more of them. But I think w.r.t. statistical methodology, the paper is troublesome or at least not good enough for ACL. In this short report, I explain why.

Critical evaluation of word similarity datasets is very important for computational lexical semantics. This short report concerns the sanity check proposed in Batchkarov et al. (2016) to evaluate several popular datasets such as MC, RG and MEN — the first two reportedly failed. I argue that this test is unstable, offers no added insight, and needs major revision in order to fulfill its purported goal.

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Similarity, co-occurrence, functional relation, part-whole relation, subcategorization, what else?

In word sense disambiguation and named-entity disambiguation, an important assumption is that a document consists of related concepts and entities.

There are millions of concepts and entities, what makes some related but not others? This question is difficult and I don’t have the definitive answer. But it is a good start to list some classes of relatedness. Continue reading