Negative Sampling Improves Hypernymy Extraction Based on Projection Learning

2017-07-12 · EACL 2017 4 · Dmitry Ustalov, Nikolay Arefyev, Chris Biemann, Alexander Panchenko

We present a new approach to extraction of hypernyms based on projection learning and word embeddings. In contrast to classification-based approaches, projection-based methods require no candidate hyponym-hypernym pairs. While it is natural to use both positive and negative training examples in supervised relation extraction, the impact of negative examples on hypernym prediction was not studied so far. In this paper, we show that explicit negative examples used for regularization of the model significantly improve performance compared to the state-of-the-art approach of Fu et al. (2014) on three datasets from different languages.

📄 PDF Abstract BibTeX arXiv:1707.03903

Code (1)

nlpub/projlearn 공식 구현 tf

Tasks

General Classification Relation ExtractionWord Embeddings

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Denoising

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