A Dependency-Based Neural Network for Relation Classification
Table 4: Performance of DepNN with different components.
['Model', 'F1 50- [ITALIC] d', 'F1 200- [ITALIC] d']
The experiment results also indicate that both the NER and WordNet features can improve the performance of relation extraction. WordNet seems less useful than NER, which conforms to the results of \newciteMoYu2014qv , since a large number of WordNet hypernyms may cause overfitting. Furthermore, the attached subtrees, as we expect, can provide an obvious boost to DepNN. The NER tags, WordNet hypernyms and subtrees all contribute to the performance by providing supplemental information for words on the shortest path. The experiments show that the subtree information does a better job than the other two kinds of information and can help build more precise representations for words in a sentence. To get a deeper understanding of what semantic information can be captured behind the ADP structure, we will look into our model and analyze it with specific examples. Since the Gigaword embeddings, with its larger corpus and dimensions, can significantly improve the classification performance, the following experiments and analysis are all based on Gigaword embeddings.