Incorporating Relation Paths in Neural Relation Extraction
Table 5: Accuracy of different models in zero-shot situation.
['Feature', 'Accuracy']
The result using CNN+rand features is comparable to the result using CNN+max features. It shows that using max operation to train the features does not greatly improve the features’ behavior in this task, even though it performs well in previous tasks. The reason is that, both CNN+rand and CNN+max only encode the information from a single sentence, and they are unable to capture the correlations between relations. (2) Feature from Path+rand/max shows its effectiveness over those from other methods. It indicates that our method is able to model the correlations between relations, while also keeps the syntactic and semantic meaning of a sentence. Therefore, the features extracted from Path+rand/max are useful for a wider range of applications, especially in those tasks which need the information from relations.