Attention-based Bi-directional Long Short-Term Memory neural network and Conditional Random Field (AttBi-LSTM-CRF) model was proposed for the corpus core entity recognition and core entity sentiment analysis task of Sohu coreEntityEmotion_train. Firstly, the text was pre-trained, each word was mapped into a low-dimensional vector with the same dimension. Then, these vectors were input into the Attention-based Bi-directional Long Short-Term Memory neural network (AttBi-LSTM) to obtain the long-term context information and focus on the information highly related to the output label. Finally, the optimal label of the entire sequence was obtained through the Conditional Random Field ( CRF) layer. The comparison experiments were conducted among AttBi-LSTM-CRF model, Bi-directional Long Short-Term Memory neural network (Bi-LSTM), AttBi-LSTM and Bi-directional Long Short-Term Memory neural network and Conditional Random Field (Bi-LSTM-CRF) model. The experimental results show that, the accuracy of AttBi-LSTM-CRF model is 0.78, the recall is 0.667, and the F1 value is 0.553, which are better than those of the comparison models. The superiority of AttBi-LSTM-CRF performance is verified.

In order to solve the problem of information loss caused by local motion blur in daily captured images or videos, a local motion detection algorithm based on region energy estimation was proposed. Firstly, the Harris feature points of the image were calculated, and alternative areas were screened out according to the distribution of feature points of each area. Secondly, according to the characteristic of smooth gradient distribution in monochromatic areas, the gradient distribution of the alternative areas was calculated and the average amplitude threshold was used to filter out most of areas which can be easily misjudged. At last, the blur direction of the alternative areas was estimated according to the energy degeneration feature of motion blur images, and the energy of the blur direction and its perpendicular direction were calculated, thus the monochrome region and defocus blur areas were further removed according to the energy ratio in both above directions. Experimental results on image data sets show that the proposed method can detect the motion blur areas from images with monochromatic areas and defocus blur areas, and effectively improve the robustness and adaptability of local motion blur detection.

For the entity recognition of commodity attributes in clothing commodity title, a hybrid method combining Conditional Random Field (CRF) with entity boundary detecting rules was proposed. Firstly, the hidden entity hint character messages were obtained through a statistical method; secondly, statistical word indicators and their implications were interpreted with a granularity of character; thirdly, entity boundary detecting rules was proposed based on the entity hint characters and statistical word indicators; finally, a method for identifying threshold values in rules was proposed based on empirical risk minimization. In the comparison experiments with character-based CRF models, the overall precision, recall and F1 score were increased by 1.61%, 2.54% and 2.08% respectively, which validated the efficiency of the entity boundary detecting rule. The proposed method can be used in e-commerce Information Retrieval (IR), e-commerce Information Extraction (IE) and query intention identification, etc.

In view of the problem that high definition stereoscopic video sequences have high resolution, less information of macro block, and network transmission error, an end-to-end transmission distortion model was proposed. Considering error diffusion between frames caused by packet loss and the characteristics of spatial and temporal correlation, the recursive algorithm could estimate distortion accurately. And the error concealment method of copying the previous one of the lost frame was mainly used in the model, reducing the dependencies of the decoder. The simulation results show that the average prediction error of the distortion model can be controlled within 6%, and this model can be adapted to estimate transmission distortion for stereo video sequences with different features and resolutions under different network environments.