This paper presents a Siamese adaptation of the Long Short-Term Memory (LSTM) network for learning sentence similarity. The model is applied to assess semantic similarity between sentences, outperforming handcrafted features and recent neural network systems. The model uses word-embedding vectors supplemented with synonymous information to encode sentence meaning. By restricting subsequent operations to a simple Manhattan metric, the model learns a structured space where sentence representations reflect complex semantic relationships. The model is trained on paired examples to learn a highly structured space of sentence representations that capture rich semantics. The model's performance is evaluated on the SICK dataset, where it outperforms existing systems across multiple metrics. The model's representations are interpretable, allowing for analysis of sentence structure and semantic relationships. The model also performs well on the semantic entailment task, demonstrating the utility of the learned representations. The model's success is attributed to its ability to learn structured representations through the use of a Manhattan metric and pre-trained word embeddings. The model's architecture is simple, making it suitable for real-time applications. The model's performance is further enhanced by synonym augmentation and pre-trained word embeddings, which help overcome the limitations of existing labeled datasets. The model's results highlight the effectiveness of LSTM-based approaches for semantic similarity and entailment tasks.This paper presents a Siamese adaptation of the Long Short-Term Memory (LSTM) network for learning sentence similarity. The model is applied to assess semantic similarity between sentences, outperforming handcrafted features and recent neural network systems. The model uses word-embedding vectors supplemented with synonymous information to encode sentence meaning. By restricting subsequent operations to a simple Manhattan metric, the model learns a structured space where sentence representations reflect complex semantic relationships. The model is trained on paired examples to learn a highly structured space of sentence representations that capture rich semantics. The model's performance is evaluated on the SICK dataset, where it outperforms existing systems across multiple metrics. The model's representations are interpretable, allowing for analysis of sentence structure and semantic relationships. The model also performs well on the semantic entailment task, demonstrating the utility of the learned representations. The model's success is attributed to its ability to learn structured representations through the use of a Manhattan metric and pre-trained word embeddings. The model's architecture is simple, making it suitable for real-time applications. The model's performance is further enhanced by synonym augmentation and pre-trained word embeddings, which help overcome the limitations of existing labeled datasets. The model's results highlight the effectiveness of LSTM-based approaches for semantic similarity and entailment tasks.