# Multi-Context Term Embeddings: the Use Case of Corpus-based Term Set Expansion

## TL;DR

This paper introduces a new algorithm that combines multi-context term embeddings with a neural classifier for improved corpus-based term set expansion, supported by a novel dataset and showing significant performance gains.

## Contribution

The paper presents a novel algorithm integrating multi-context embeddings with neural classification and introduces a unique dataset for intrinsic evaluation.

## Key findings

- Up to 5 MAP points improvement over baseline
- Effective combination of multi-context embeddings and neural classifiers
- New dataset for intrinsic evaluation of term set expansion

## Abstract

In this paper, we present a novel algorithm that combines multi-context term embeddings using a neural classifier and we test this approach on the use case of corpus-based term set expansion. In addition, we present a novel and unique dataset for intrinsic evaluation of corpus-based term set expansion algorithms. We show that, over this dataset, our algorithm provides up to 5 mean average precision points over the best baseline.