Neural Open Relation Tagging Model
Table 3. The results of evaluating the influence of embeddings used on the model HNN4ORT. The results are average on the Reverb dataset, Wikipedia dataset, and NYT dataset.
['Model', 'P', 'R', 'F']
When the model HNN4ORT only uses word embeddings, the results are worse than that of the model with word embeddings and POS embeddings. We certificate that the POS features play a great role in promoting model performances and improve the F1 by 4.1%. In addition, when the input embeddings are adjusted along with model training, the effect is better and the F1 is increased by 1.4%.