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Several language applications often require word semantics as a core part oftheir processing pipeline, either as precise meaning inference or semanticsimilarity. Multi-sense embeddings (M-SE) can be exploited for this importantrequirement. M-SE seeks to represent each word by their distinct senses inorder to resolve the conflation of meanings of words as used in differentcontexts. Previous works usually approach this task by training a model on alarge corpus and often ignore the effect and usefulness of the semanticrelations offered by lexical resources. However, even with large training data,coverage of all possible word senses is still an issue. In addition, aconsiderable percentage of contextual semantic knowledge are never learnedbecause a huge amount of possible distributional semantic structures are neverexplored. In this paper, we leverage the rich semantic structures in WordNetusing a graph-theoretic walk technique over word senses to enhance the qualityof multi-sense embeddings. This algorithm composes enriched texts from theoriginal texts. Furthermore, we derive new distributional semantic similaritymeasures for M-SE from prior ones. We adapt these measures to word sensedisambiguation (WSD) aspect of our experiment. We report evaluation results on11 benchmark datasets involving WSD and Word Similarity tasks and show that ourmethod for enhancing distributional semantic structures improves embeddingsquality on the baselines. Despite the small training data, it achievesstate-of-the-art performance on some of the datasets.

EDS-MEMBED: Multi-sense embeddings based on enhanced distributional semantic structures via a graph walk over word senses