Dating Documents using Graph Convolution Networks
Table 2: Accuracies of different methods on APW and NYT datasets for the document dating problem (higher is better). NeuralDater significantly outperforms all other competitive baselines. This is our main result. Please see Section 7.1 for more details.
['Method', 'APW', 'NYT']
In order to evaluate the effectiveness of NeuralDater, our proposed method, we compare it against existing document dating systems and text classification models. Overall, we find that NeuralDater outperforms all other methods with a significant margin on both datasets. Compared to the previous state-of-the-art in document dating, BurstySimDater Kotsakos et al. This may be attributed to the fact that the model utilizes only year mentions in the document, thus ignoring other relevant signals which might be relevant to the task. BurstySimDater performs considerably better in terms of precision compared to the other baselines, although it significantly underperforms in accuracy. We note that NeuralDater outperforms all these prior models both in terms of precision and accuracy. However, since such a model doesn’t give specific attention to temporal features in the document, its performance remains limited.