On the Learnability of Concepts With Applications to Comparing Word Embedding Algorithms
Table 2: Average Performance of Linear Classifiers using LIWC word lists on randomly generated word embeddings to identify members of its own set.
['[BOLD] L', 'Size', 'Accuracy', 'Recall', 'FPR', 'Prec', 'AUC']
As shown in Tab. All word lists perform equally as random in predicting members of the concept that it is representing. This confirms that random embeddings are unable to capture semantic information in its embedding space, confirming our hypothesis.