Non-distributional Word Vector Representations
Таблица 3: Performance of different type of word vectors on evaluation tasks reported by Spearman’s correlation (first 3 columns) and Accuracy (last 2 columns). Bold shows the best performance for a task.
['Vector', 'Length ( [ITALIC] D)', 'Params.', 'Corpus Size', 'WS-353', 'RG-65', 'SimLex', 'Senti', 'NP']
It can be seen that although Skip-Gram, Glove & LSA perform better than linguistic vectors on WS-353, the linguistic vectors outperform them by a huge margin on SimLex. Linguistic vectors also perform better at RG-65. On sentiment analysis, linguistic vectors are competitive with Skip-Gram vectors and on the NP-bracketing task they outperform all distributional vectors with a statistically significant margin (p < 0.05, McNemar’s test \newciteDietterich98). The combined vector outperforms Skip-Gram on all tasks, showing that linguistic vectors contain useful information orthogonal to distributional information.