  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.

Computation and Language;Machine Learning