Abstract:Fine-grained entity typing is important to tasks like relation extraction and knowledge base construction. We find however, that finegrained entity typing systems perform poorly on general entities (e.g. "ex-president") as compared to named entities (e.g. "Barack Obama"). This is due to a lack of general entities in existing training data sets. We show that this problem can be mitigated by automatically generating training data from WordNets. We use a German WordNet equivalent, Ger-maNet, to automatically gene… Show more
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