Sentence representations have become a critical component in natural language processing applications, such as retrieval, question answering, and text classification. They capture the semantics and meaning of a sentence, enabling machines to understand and reason over human language. In recent years, significant progress has been made in developing methods for learning sentence representations, including unsupervised, supervised, and transfer learning approaches. In this paper, we provide an overview of the different methods for sentence representation learning, including both traditional and deep learning-based techniques. We provide a systematic organization of the literature on sentence representation learning, highlighting the key contributions and challenges in this area. Overall, our review highlights the progress made in sentence representation learning, the importance of this area in natural language processing, and the challenges that remain. We conclude with directions for future research, suggesting potential avenues for improving the quality and efficiency of sentence representations in NLP applications.

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