On the Learnability of Concepts With Applications to Comparing Word Embedding Algorithms
Table 4: Average Performance of Linear Classifiers using LIWC word lists on word2vec embeddings to identify members of its own set. Random lists are also tested to obtain a p-value and compare performances. word2vec is the embedding algorithm used. These embeddings perform better than random embeddings, after one thousand iterations and random word lists resulting in a p-value of <0.001
['[BOLD] L', 'Size', 'Accuracy', 'Recall', 'FPR', 'Prec', 'AUC']
Tab. Lrandom(avg) and Lrandom(max) again show the average and best performances of random word lists.