SSE: Semantic Sentence Embedding for learning user interactions
J. Felicia Lilian, K. Sundarakantham, R. Harine Rajashree, S. Mercy Shalinie · 2019
Semantics is an integral component in NLP. Semantics provides a meaningful view about the meaning of the language. The meaning of the text is susceptible to the negative words present in them. Thus the impact of semantics in NLP in turn increases the impact of negative words in the sentence. It plays crucial role in shaping the semantics of any sentence. Although all the sub-areas of natural language processing try to find out the impact of negative words on their implementation, the effect of these words are not considered in the word embedding process. Being the foremost step in NLP the omission of these words in embedding can affect the representation of the language. The aim is to propose a model which produces word embeddings by considering the negative words. Although most NLP implementing systems consider all negative words this paper focuses on the word `not' which is followed by an adjective. The antonym for the adjective is identified and is then replaced in the corpus. The model is achieved by replacing the corpus where negative words and this corpus is fed to a three layer neural network to form the embeddings. The proposed technique proves an accuracy of 93.6% in retrieving the relevant answer to the user.