On the Information Content of Predictions in Word Analogy Tests
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Abstract
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.
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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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Regular Papers
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