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On the Information Content of Predictions in Word Analogy Tests. (arXiv:2210.09972v1 [cs.CL])
Oct. 19, 2022, 1:12 a.m. | Jugurta Montalvão
cs.LG updates on arXiv.org arxiv.org
An approach is proposed to quantify, in bits of information, the actual
relevance of analogies in analogy tests. The main component of this approach is
a softaccuracy estimator that also yields entropy estimates with compensated
biases. Experimental results obtained with pre-trained GloVe 300-D vectors and
two public analogy test sets show that proximity hints are much more relevant
than analogies in analogy tests, from an information content perspective.
Accordingly, a simple word embedding model is used to predict that analogies …
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