# SJTU-NLP at SemEval-2018 Task 9: Neural Hypernym Discovery with Term Embeddings

arXiv: https://arxiv.org/abs/1805.10465 · HTML: https://mlsanity.com/paper/1805.10465/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 May 2018.

## Built on by (most cited first, 1 of 1)

- [HyperBox: A Supervised Approach for Hypernym Discovery using Box Embeddings](https://mlsanity.com/paper/2204.02058/) (2022)

## Abstract (arXiv)

This paper describes a hypernym discovery system for our participation in the SemEval-2018 Task 9, which aims to discover the best (set of) candidate hypernyms for input concepts or entities, given the search space of a pre-defined vocabulary. We introduce a neural network architecture for the concerned task and empirically study various neural network models to build the representations in latent space for words and phrases. The evaluated models include convolutional neural network, long-short term memory network, gated recurrent unit and recurrent convolutional neural network. We also explore different embedding methods, including word embedding and sense embedding for better performance.