Auto-Encoding Dictionary Definitions into Consistent Word Embeddings
Monolingual dictionaries are widespread and semantically rich resources. This paper presents a simple model that learns to compute word embeddings by processing dictionary definitions and trying to reconstruct them. It exploits the inherent recursivity of dictionaries by encouraging consistency between the representations it uses as inputs and the representations it produces as outputs. The resulting embeddings are shown to capture semantic similarity better than regular distributional methods and other dictionary-based methods. In addition, our method shows strong performance when trained exclusively on dictionary data and generalizes in one shot.
PDF AbstractCode
Datasets
Add Datasets
introduced or used in this paper
Results from the Paper
Submit
results from this paper
to get state-of-the-art GitHub badges and help the
community compare results to other papers.
Methods
No methods listed for this paper. Add
relevant methods here
