Neural Network Pun Material Identifiction Framework based on Artificial Intelligence Learning

Abstract

The Neural based artificial intelligence system is made linguistically intelligent through simulation model to identify pun material expressions from code mixed text. The text available on social media consist contents which are written in mixed script format and from these content puns word identification is a challenging task in this scenario. The retrieval of pun and its corresponding equivocation terms is very hard to retrieve from the transliterated text. The pun retrieval and its equivocation representation are widely used to present the opinions over the network applications. The work described in the paper gives the comparative view of different neural network and its corresponding learning and simulation techniques applied in the area of transliteration. The rule framed approach is presented which accepts the roman form text as input and as per the defined rules the system is developed to give the equivocation words available in the sentence. The evaluation measures used here to validate the hypothesis is based on statistical measures along with HLSTM learning model. Further the result is validated using the voting technique that can choose appropriate equivocation label which are not identifies by the learning model. The use of voting technique here is to provide an extra edge when the proposed approaches suggest incorrect tag against the pun word. The voting approach enhances the overall result accuracy with high precision value.