Incorporating Word Embeddings into Open Directory Project based Large-scale Classification
Table 5: Classification Performance on the NYT Dataset (2,735 categories).
['Precision at  [ITALIC] k  [ITALIC] k', 'Precision at  [ITALIC] k 1', 'Precision at  [ITALIC] k 2', 'Precision at  [ITALIC] k 3', 'Precision at  [ITALIC] k 4', 'Precision at  [ITALIC] k 5']
Again, the performance of ODPCV+WV outperforms ODP, PV, CNN, ODPCV and ODPWV over 28%, 119%, 216%, 12%, and 10% in terms of precision at k on average, respectively. We also observe that both ODPCV and ODPWV outperform ODP. These results clearly demonstrate that both category and word vectors are effective at text classification. Specifically, ODPCV+WV, which utilizes both category and word vectors, achieves the best performance in all experiments. We also perform the t-test for the classification results, and find that ODPCV+WV results are statistically significant with p \textless 0.01.