A Review on Identification of Contextual Similar Sentences

The task of identifying contextual similar sentences plays a crucial role in various natural language processing applications such as information retrieval, paraphrase detection, and question answering systems. This paper presents a comprehensive review of the methodologies, techniques, and advancements in the identification of contextual similar sentences. Beginning with an overview of the importance and challenges associated with this task, the paper delves into the various approaches employed, including traditional similarity metrics, deep learning architectures, and transformer-based models. Furthermore, the review explores different datasets and evaluation metrics used to assess the performance of these methods. Additionally, the paper discusses recent trends, emerging research directions, and potential applications in the field. By synthesizing existing literature, this review aims to provide researchers and practitioners with insights into the state-of-the-art techniques and future avenues for advancing the identification of contextual similar sentences

Detecting Semantic-based Similarity Between Verses of The Quran with Doc2vec

Semantic similarity analysis in natural language texts is getting great attention recently. Semantic analysis of the Quran is especially challenging because it is not simply factual but encodes subtle religious meanings. Investigating similarity and relatedness between the Quranic verses is a hot topic and can promote the acquisition of the underlying knowledge. Therefore, we use an NPL method to detect the semantic-based similarity between the verses of the Quran. The idea is to exploit the distributed representation of text, to learn an informative representation of the Quran’s passages. We map the Arabic Quranic verses to numerical vectors that encode the semantic properties of the text. We then measure similarity among those vectors. The performance of our model is judged through cosine similarity between our assigned semantic similarity scores and annotated textual similarity datasets. Our model scored 76% accuracy on detecting the similarity, and it can act as a basis for potential experiments and research