Attention based recurrent neural networks (RNN) have shown a great success for question answering (QA) in recent years. Although significant improvements have been achieved over the non-attentive models, the position information is not well studied within the attention-based framework. Motivated by the effectiveness of using the word positional context to enhance information retrieval, we assume that if a word in the question (i.e., question word) occurs in an answer sentence, the neighboring words should be given more attention since they intuitively contain more valuable information for question answering than those far away. Based on this assumption, we propose a positional attention based RNN model, which incorporates the positional context of the question words into the answers' attentive representations. Experiments on two benchmark datasets show the great advantages of our proposed model. Specifically, we achieve a maximum improvement of 8.83% over the classical attention based RNN model in terms of mean average precision. Furthermore, our model is comparable to if not better than the state-of-the-art approaches for question answering.

This paper focus on two phenomena that "same spectrum with different objects" and "same object with different spectra" in multispectral remote sensing image, and propose a stepwise refinement classification method based on multi-sensitive strategies. It's a top-down, gradually refinement hierarchical way of classification which combines with advantages of both supervised classification and unsupervised classification: by analyzing the characteristic of spectrum curve, cluster and find out the band combinations with big characteristic differences as the guidance of classification; according to spectral characteristics of different bands combinations, choose different methods for further fine classification. Experimental results show that the proposed method achieved the high accurate classification of multispectral remote sensing images and effectively overcome the both above phenomena. Furthermore, the overall accuracy and kappa coefficient also confirm its superior performance.