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On the Information Content of Predictions in Word Analogy Tests
2022
Journal of Communication and Information Systems
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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
doi:10.14209/jcis.2022.18
fatcat:fnbwmcev6zbtzlndyyoizgnr5i