Enhancing Word Embeddings with Knowledge Extracted from Lexical Resources
This work addresses the need for more semantically accurate word representations in natural language processing, though it is incremental as it builds on existing specialization methods.
The authors tackled the problem of improving word embeddings by incorporating external knowledge from lexical resources like BabelNet, resulting in gains over state-of-the-art methods on word similarity and dialog state tracking tasks.
In this work, we present an effective method for semantic specialization of word vector representations. To this end, we use traditional word embeddings and apply specialization methods to better capture semantic relations between words. In our approach, we leverage external knowledge from rich lexical resources such as BabelNet. We also show that our proposed post-specialization method based on an adversarial neural network with the Wasserstein distance allows to gain improvements over state-of-the-art methods on two tasks: word similarity and dialog state tracking.