Papers › Leveraging WordNet Paths for Neural Hypernym Prediction
Leveraging WordNet Paths for Neural Hypernym Prediction
Yejin Cho, Juan Diego Rodriguez, Yifan Gao, Katrin Erk
We formulate the problem of hypernym prediction as a sequence generation task, where the sequences are taxonomy paths in WordNet. Our experiments with encoder-decoder models show that training to generate taxonomy paths can improve the performance of direct hypernym prediction. As a simple but powerful model, the hypo2path model achieves state-of-the-art performance, outperforming the best benchmark by 4.11 points in hit-at-one (H@1).
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