Multi-Task Identification of Entities, Relations, and Coreference for Scientific Knowledge Graph Construction
Table 4: Results for scientific keyphrase extraction and extraction on SemEval 2017 Task 10, comparing with previous best systems.
['Model', 'Span Indentification P', 'Span Indentification R', 'Span Indentification F1', 'Keyphrase Extraction P', 'Keyphrase Extraction R', 'Keyphrase Extraction F1', 'Relation Extraction P', 'Relation Extraction R', 'Relation Extraction F1', 'Overall P', 'Overall R', 'Overall F1']
dataset for tasks of span identification, keyphrase extraction and relation extraction as well as the overall score. Span identification aims at identifying spans of entities. Keyphrase classification and relation extraction has the same setting with the entity and relation extraction in SciERC. Our model outperforms all the previous models that use hand-designed features. We observe more significant improvement in span identification than keyphrase classification. This confirms the benefit of our model in enumerating spans (rather than BIO tagging in state-of-the-art systems). Moreover, we have competitive results compared to the previous state of the art in relation extraction. We observe less gain compared to the SciERC dataset mainly because there are no coference links, and the relation types are not comprehensive.