Type 2 Possibility Factor Rotation in No-Data Problem

Uemura [1] discovered a mapping formula that transforms and maps the state of nature into fuzzy events with a membership function that expresses the degree of attribution. In decision theory in no-data problems, sequential Bayesian inference is an example of this mapping formula, and Hori et al. [2] made the mapping formula multidimensional, introduced the concept of time, to Markov (decision) processes in fuzzy events under ergodic conditions, and derived stochastic differential equations in fuzzy events, although in reverse. In this paper, we focus on type 2 fuzzy. First, assuming that Type 2 Fuzzy Events are transformed and mapped onto the state of nature by a quadratic mapping formula that simultaneously considers longitudinal and transverse ambiguity, the joint stochastic differential equation representing these two ambiguities can be applied to possibility principal factor analysis if the weights of the equations are orthogonal. This indicates that the type 2 fuzzy is a two-dimensional possibility multivariate error model with longitudinal and transverse directions. Also, when the weights are oblique, it is a general possibility oblique factor analysis. Therefore, an example of type 2 fuzzy system theory is the possibility factor analysis. Furthermore, we show the initial and stopping condition on possibility factor rotation, on the base of possibility theory.

Reliable pseudo-labeling prediction framework for new event type induction

As a subtask of open domain event extraction(ODEE),new event type induction aims to discover a set of unseen event types from a given corpus.Existing methods mostly adopt semi-supervised or unsupervised learning to achieve the goal,which uses complex and different objective functions for labeled and unlabeled data respectively.In order to unify and simplify objective functions,a reliable pseudo-labeling prediction(RPP)framework for new event type induction was proposed.The framework introduces a double label reassignment(DLR)strategy for unlabeled data based on swap-prediction.DLR strategy can alleviate the model degeneration caused by swap-predication and further combine the real distribution over unseen event types to produce more reliable pseudo labels for unlabeled data.The generated reliable pseudo labels help the overall model be optimized by a unified and simple objective.Experiments show that RPP framework outperforms the state-of-the-art on the benchmark.