01730nas a2200229   4500000000100000000000100001008004100002260001200043653001800055653002700073653002000100653002000120653001700140100003400157700001800191245008400209856008000293300001000373490000600383520109700389022001401486       2020                            d  c12/202010aHypernym Path10aMulti-sense Embeddings10aWord Embeddings10aWord Similarity10aSynonym Sets1 aKrishna Siva Prasad Mudigonda1 aPoonam Sharma00aMulti-sense Embeddings Using Synonym Sets and Hypernym Information from Wordnet  uhttps://www.ijimai.org/journal/sites/default/files/2020-11/ijimai_6_4_7.pdf  a68-790 v63 aWord embedding approaches increased the efficiency of natural language processing (NLP) tasks. Traditional word embeddings though robust for many NLP activities, do not handle polysemy of words. The tasks of semantic similarity between concepts need to understand relations like hypernymy and synonym sets to produce efficient word embeddings. The outcomes of any expert system are affected by the text representation. Systems that understand senses, context, and definitions of concepts while deriving vector representations handle the drawbacks of single vector representations. This paper presents a novel idea for handling polysemy by generating Multi-Sense Embeddings using synonym sets and hypernyms information of words. This paper derives embeddings of a word by understanding the information of a word at different levels, starting from sense to context and definitions. Proposed sense embeddings of words obtained prominent results when tested on word similarity tasks. The proposed approach is tested on nine benchmark datasets, which outperformed several state-of-the-art systems.  a1989-1660