# Integrating Lexical Knowledge in Word Embeddings using Sprinkling and Retrofitting

arXiv: https://arxiv.org/abs/1912.06889 · HTML: https://mlsanity.com/paper/1912.06889/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Dec 2019.

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

- [Lexical semantics enhanced neural word embeddings](https://mlsanity.com/paper/2210.00754/) (2022)

## Abstract (arXiv)

Neural network based word embeddings, such as Word2Vec and GloVe, are purely data driven in that they capture the distributional information about words from the training corpus. Past works have attempted to improve these embeddings by incorporating semantic knowledge from lexical resources like WordNet. Some techniques like retrofitting modify word embeddings in the post-processing stage while some others use a joint learning approach by modifying the objective function of neural networks. In this paper, we discuss two novel approaches for incorporating semantic knowledge into word embeddings. In the first approach, we take advantage of Levy et al's work which showed that using SVD based methods on co-occurrence matrix provide similar performance to neural network based embeddings. We propose a 'sprinkling' technique to add semantic relations to the co-occurrence matrix directly before factorization. In the second approach, WordNet similarity scores are used to improve the retrofitting method. We evaluate the proposed methods in both intrinsic and extrinsic tasks and observe significant improvements over the baselines in many of the datasets.