This paper proposes an Attention-Based Bidirectional Long Short-Term Memory Networks (Att-BLSTM) model for relation classification in natural language processing (NLP). The model uses a neural attention mechanism with Bidirectional Long Short-Term Memory Networks (BLSTM) to capture the most important semantic information in a sentence. Unlike traditional methods that rely on lexical resources or NLP systems to extract high-level features, Att-BLSTM directly processes raw text with position indicators as input. The model is evaluated on the SemEval-2010 relation classification task, achieving an F1-score of 84.0%, which outperforms most existing methods. Relation classification involves identifying semantic relations between pairs of nominals, which is useful for NLP applications such as information extraction and question answering. Traditional methods often use handcrafted features from lexical resources or NLP systems, which can be time-consuming and computationally expensive. Deep learning methods, such as convolutional neural networks (CNN) and recurrent neural networks (RNN), have been proposed to automatically learn features, but they still rely on lexical resources or NLP systems. The Att-BLSTM model consists of five components: input layer, embedding layer, LSTM layer, attention layer, and output layer. The embedding layer maps each word into a low-dimensional vector. The LSTM layer uses BLSTM to extract high-level features from the input sentence. The attention layer produces a weight vector to merge word-level features into a sentence-level feature vector. The output layer uses the sentence-level feature vector for relation classification. The model is trained using AdaDelta with a learning rate of 1.0 and a minibatch size of 10. The model parameters are regularized with L2 regularization. The model achieves an F1-score of 84.0% on the SemEval-2010 dataset, outperforming existing methods. The model is simple and effective, as it does not rely on lexical resources or NLP systems to extract features.This paper proposes an Attention-Based Bidirectional Long Short-Term Memory Networks (Att-BLSTM) model for relation classification in natural language processing (NLP). The model uses a neural attention mechanism with Bidirectional Long Short-Term Memory Networks (BLSTM) to capture the most important semantic information in a sentence. Unlike traditional methods that rely on lexical resources or NLP systems to extract high-level features, Att-BLSTM directly processes raw text with position indicators as input. The model is evaluated on the SemEval-2010 relation classification task, achieving an F1-score of 84.0%, which outperforms most existing methods. Relation classification involves identifying semantic relations between pairs of nominals, which is useful for NLP applications such as information extraction and question answering. Traditional methods often use handcrafted features from lexical resources or NLP systems, which can be time-consuming and computationally expensive. Deep learning methods, such as convolutional neural networks (CNN) and recurrent neural networks (RNN), have been proposed to automatically learn features, but they still rely on lexical resources or NLP systems. The Att-BLSTM model consists of five components: input layer, embedding layer, LSTM layer, attention layer, and output layer. The embedding layer maps each word into a low-dimensional vector. The LSTM layer uses BLSTM to extract high-level features from the input sentence. The attention layer produces a weight vector to merge word-level features into a sentence-level feature vector. The output layer uses the sentence-level feature vector for relation classification. The model is trained using AdaDelta with a learning rate of 1.0 and a minibatch size of 10. The model parameters are regularized with L2 regularization. The model achieves an F1-score of 84.0% on the SemEval-2010 dataset, outperforming existing methods. The model is simple and effective, as it does not rely on lexical resources or NLP systems to extract features.