We use both reinforcement learning and deep learning to simultaneously extract entities and relations from unstructured texts. For reinforcement learning, we model the task as a two-step decision process. Deep learning is used to automatically capture the most important information from unstructured texts, which represent the state in the decision process. By designing the reward function per step, our proposed method can pass the information of entity extraction to relation extraction and obtain feedback in order to extract entities and relations simultaneously. Firstly, we use bidirectional LSTM to model the context information, which realizes preliminary entity extraction. On the basis of the extraction results, attention based method can represent the sentences that include target entity pair to generate the initial state in the decision process. Then we use Tree-LSTM to represent relation mentions to generate the transition state in the decision process. Finally, we employ Q-Learning algorithm to get control policy π in the two-step decision process. Experiments on ACE2005 demonstrate that our method attains better performance than the state-of-the-art method and gets a 2.4% increase in recall-score.

Due to the fact that individual knowledge is often inadequate and deficient in group decision-making problems, it is commonly seen that the preference information provided by the decision makers are incomplete. To encounter this problem lots of relevant researches have already been carried out. However, previous methods often only take into account either the information of the expert who provided incomplete preference, or information provided by the rest of the group. To be more thorough and comprehensive, this paper combined both parts of the information, by first setting the unknown preference values as output and known ones as inputs, training the Bayesian regression model with the complete observations of other experts to detect the associations between them, and then putting in the incomplete preference information to predict the unknown ones. Moreover, inspired by the empirical rule of the Gaussian distribution, this paper also propose to combine the concept of confidence interval with that of interval-valued preference relations, by expressing the prediction results with interval-valued fuzzy reciprocal relations to allow some degree of uncertainty. On the basis of that an iterative consensus reaching process incorporated with feedback mechanism is also proposed. Finally a case study of possible application of our proposed model in project performance evaluations is carried out, the results of which then further verify the practicality and validity of our model.

This paper investigates the consistency definition and the weight-deriving method for additive interval fuzzy preference relations (IFPRs) using a particular characterization based on logarithms. In a recently published paper, a new approach with a parameter is developed to obtain priority weights from fuzzy preference relations (FPRs), then a new consistency definition for the additive IFPRs is defined, and finally linear programming models for deriving interval weights from consistent and inconsistent IFPRs are proposed. However, the discussion of the parameter value is not adequate and the weights obtained by the linear models for inconsistent IFPRs are dependent on alternative labels and not robust to permutations of the decision makers’ judgments. In this paper, we first investigate the value of the parameter more thoroughly and give the closed form solution for the parameter. Then, we design a numerical example to illustrate the drawback of the linear models. Finally, we construct a linear model to derive interval weights from IFPRs based on the additive transitivity based consistency definition. To demonstrate the effectiveness of our proposed method, we compare our method to the existing method on three numerical examples. The results show that our method performs better on both consistent and inconsistent IFPRs.