Traditional user intention recognition methods in chatting robot are usually based on template matching or artificial feature sets. To address the problem that those methods are difficult, time-consuming but have a week extension, an intention recognition model based on Biterm Topic Model (BTM) and Bidirectional Gated Recurrent Unit (BiGRU) was proposed with considering the features of the chatting texts about health. The identification of user's medical intention was regarded as a classification problem and topic features were used in the hybrid model. Firstly, the topic of user's every chatting sentence was mined by BTM with quantification. Then last step's results were fed into BiGRU to do context-based learning for getting the final representation of user's continuous statements. At last, the task was finished by making classification. In the comparison experiments on crawling corpus, the BTM-BiGRU model obviously outperforms to other traditional methods such as Support Vector Machine (SVM), even the F value approximately increses by 1.5 percentage points compared to the state-of-the-art model combining Convolution Neural Network and Long-Short Term Memory Network (CNN-LSTM). Experimental results show that the proposed method can effectively improve the accuracy of the intention recognition focusing on characteristics of the study.

The inconformity of user evaluation standard and preference results in unfair comparability between online services in cyberspace, thereby the users are hardly to choose satisfactory online services. The ranking method to calculate the online service quality based on social choice theory was proposed. First, group preference matrix was built according to the user-service evaluation matrix given by users; second, 0-1 integer programming model was built based on group preference matrix and Kemeny social choice function; at last, the optimal service ranking results could be obtained by solving this model. The individual preferences were aggregated to group preference in the proposed method; the decision was consistent with the majority preference of the group and maximum consistency with the individual preference. The proposed method's rationality and effectiveness were verified by theoretical analysis and experiment results. The experimental results show that the proposed method can solve the incomparability between online services, realize the online service quality ranking, effectively resisted the recommendation attacks. So it has strong anti-manipulation.

In the relation extraction of Chinese resources, the long Chinese sentence style is complex, the syntactic feature extraction is very difficult, and its accuracy is low. A bilingual cooperative relation extraction method based on a parallel corpus was proposed to resolve these above problems. In a Chinese and English bilingual parallel corpus, the English relation extraction classification was trained by dependency syntactic features which obtained by mature syntax analytic tools of English, the Chinese relation extraction classification was trained by n-gram feature which is suitable for Chinese, then they constituted bilingual view. Finally, based on the annotated and mapped parallel corpus, the training corpus with high reliability of both classifications were added to each other for bilingual collaborative training, and a Chinese relation extraction classification model with better performance was acquired. Experimental results on Chinese test corpus show that the proposed method improves the performance of Chinese relation extraction method based on weak supervision, its F value is increased by 3.9 percentage points.

In view of the real-time, sparse and massive characteristics of micro-blog, a topic discovery model based on real-time co-occurrence network was proposed. Firstly, the set of keywords was extracted from the primitive data by the model, and the relationship weights was calculated on the basis of the time parameter to structure the word co-occurrence network. Then, sparsity could be reduced by finding potential features of a strong correlation based on weight adjustment coefficient. Secondly, the topic incremental clustering could be achieved by using the improved Single-Pass algorithm. Finally, the feature words of each topic were sorted by heat calculation, so the most representative keywords of the topic were got. The experimental results show that the accuracy and comprehensive index of the proposed model increase 6%, 8% respectively compared with the Single-Pass algorithm. The experimental results prove the validity and accuracy of the proposed model.

In order to solve the problem of misjudgment which due to emotion point to an unknown and missing hidden view in traditional emotion classification method, a text sentiment classification method based on emotional role modeling was proposed. The method firstly identified evaluation objects in the text, and it used the measure based on local semantic analysis to tag the sentence emotion which had potential evaluation object. Then it distinguished the positive and negative polarity of evaluation objects in this paper by defining its emotional role. And it let the tendency value of emotional role integrate into feature space to improve the feature weight computation method. Finally, it proposed the concept named "features converge" to reduce the dimension of model. The experimental results show that the proposed method can improve the effect and accuracy of 3.2% for text sentiment classification effectively compared with other approaches which tend to pick the strong subjective emotional items as features.