A hybrid approach of Weighted Fine-Tuned BERT extraction with deep Siamese Bi – LSTM model for semantic text similarity identification

The conventional semantic text-similarity methods requires high amount of trained labeled data and also human interventions. Generally, it neglects the contextual-information and word-orders information resulted in data sparseness problem and latitudinal-explosion issue. Recently, deep-learning methods are used for determining text-similarity. Hence, this study investigates NLP application tasks usage in detecting text-similarity of question pairs or documents and explores the similarity score predictions. A new hybridized approach using Weighted Fine-Tuned BERT Feature extraction with Siamese Bi-LSTM model is implemented. The technique is employed for determining question pair sets using Semantic-text-similarity from Quora dataset. The text features are extracted using BERT process, followed by words embedding with weights. The features along with weight values, are represented as embedded vectors, are subjected to various layers of Siamese Networks. The embedded vectors of input text features were trained by using Deep Siamese Bi-LSTM model, in various layers. Finally, similarity scores are determined for each sentence, and the semantic text-similarity is learned. The performance evaluation of proposed-framework is established with respect to accuracy rate, precision value, F1 score data and Recall values parameters compared with other existing text-similarity detection methods. The proposed-framework exhibited higher efficiency rate with 91% in accuracy level in determining semantic-text-similarity compared with other existing algorithms.