Main Article Content
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 soft accuracy 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 carry about two bits of information, which is experimentally corroborated.
How to Cite
Montalvão, J. (2022). On the Information Content of Predictions in Word Analogy Tests. Journal of Communication and Information Systems, 37(1), 175–181. https://doi.org/10.14209/jcis.2022.18
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