Transformer induced enhanced feature engineering for contextual similarity detection in text

Availability of large data storage systems has resulted in digitization of information. Question and answering communities like Quora and stack overflow take advantage of such systems to provide information to users. However, as the amount of information stored gets larger, it becomes difficult to keep track of the existing information, especially information duplication. This work presents a similarity detection technique that can be used to identify similarity levels in textual data based on the context in which the information was provided. This work presents the transformer based contextual similarity detection (TCSD), which uses a combination of bidirectional encoder representations from transformers (BERT) and similarity metrics to derive features from the data. The derived features are used to train the ensemble model for similarity detection. Experiments were performed using the Quora question similarity data set. Results and comparisons indicate that the proposed model exhibits similarity detection with an accuracy of 92.5%, representing high efficiency.