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Neural network based word embeddings, such as Word2Vec and GloVe, are purelydata driven in that they capture the distributional information about wordsfrom the training corpus. Past works have attempted to improve these embeddingsby incorporating semantic knowledge from lexical resources like WordNet. Sometechniques like retrofitting modify word embeddings in the post-processingstage while some others use a joint learning approach by modifying theobjective function of neural networks. In this paper, we discuss two novelapproaches for incorporating semantic knowledge into word embeddings. In thefirst approach, we take advantage of Levy et al's work which showed that usingSVD based methods on co-occurrence matrix provide similar performance to neuralnetwork based embeddings. We propose a 'sprinkling' technique to add semanticrelations to the co-occurrence matrix directly before factorization. In thesecond approach, WordNet similarity scores are used to improve the retrofittingmethod. We evaluate the proposed methods in both intrinsic and extrinsic tasksand observe significant improvements over the baselines in many of thedatasets.

Integrating Lexical Knowledge in Word Embeddings using Sprinkling and Retrofitting