Integrating Lexical Knowledge in Word Embeddings using Sprinkling and Retrofitting

TL;DR

This paper introduces two novel methods for enhancing word embeddings by integrating lexical semantic knowledge, using a sprinkling technique on co-occurrence matrices and improved retrofitting with WordNet scores, leading to better performance.

Contribution

The paper presents two new approaches for incorporating semantic knowledge into word embeddings, combining matrix factorization and retrofitting with lexical resources.

Findings

Significant improvements in intrinsic evaluation tasks.

Enhanced performance in extrinsic NLP tasks.

Effective integration of lexical knowledge into embeddings.

Abstract

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.