Negative Sampling Improves Hypernymy Extraction Based on Projection Learning
This work addresses hypernym extraction for NLP applications, presenting an incremental improvement through negative sampling.
The paper tackles hypernym extraction by introducing a projection learning approach with explicit negative examples for regularization, achieving significant performance improvements over the state-of-the-art method on three multilingual datasets.
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.