A Joint Model for Definition Extraction with Syntactic Connection and Semantic Consistency
Table 5: The model’s performance with different numbers of layers for GCN when DPP is excluded.
['[EMPTY]', 'Textbook P', 'Textbook R', 'Textbook F1', 'Contract P', 'Contract R', 'Contract F1']
Interestingly, the dependency path prediction (i.e., DPP) seems to contribute the most to the performance of the proposed model as its absence would lead to the largest performance loss of the model (i.e., a F1 reduction of 2.5% on Textbook and 3.5% on Contract). The goal of DPP is to capture the important context words between the terms and definitions that might be far away from each other via the dependency structures. As the GCN also relies on the dependency structures to model the syntactically neighboring context words at each layer, a natural question is whether we can replace DPP with a deeper GCN model (i.e., more layers) to increase the dependency coverage (i.e., more hops) for each word and implicitly encode the dependency paths between the terms and definitions. As we can see from the tables, when the DPP component is not incorporated, simply increasing the depth of the GCN module is not sufficient to model the dependency paths between the terms and definitions (i.e., the performance of the model in such cases are significantly worse than those of the initially proposed model). This clearly demonstrates the benefits of the dependency path prediction proposed in this work for DE.