Candidate region aware nested named entity recognition.

TL;DR: In this article , a simple and effective method of gradient activation is applied to the LSTM, while empirical criteria for choosing gradient activation hyperparameters are found, and comparative experiments are conducted, and their results show that the gradient activation alleviates the ill-conditioned problem and accelerates the convergence.

TL;DR: Zhang et al. as discussed by the authors proposed KDAC, which is an aggregation function with multiple conversion modes to solve the problem of nonlinear, stable near-linear transformation and derivative, as well as dynamic style.

TL;DR: A progressive multitype feature fusion model to recognize Chinese named entity of unstructured maintenance records for power primary equipment and CRF is introduced to handle the dependencies among prediction labels and to output the optimal prediction sequence, which can easily obtain structured data of maintenance records.

TL;DR: This paper proposed a new approach for summary obfuscation detection based on named entity recognition and dependency parsing, which is straightforward but accurate and easy to analyze compared to genetic algorithm-based methods.

TL;DR: A novel, efficient, gradient based method called long short-term memory (LSTM) is introduced, which can learn to bridge minimal time lags in excess of 1000 discrete-time steps by enforcing constant error flow through constant error carousels within special units.

TL;DR: In this article, the adaptive estimates of lower-order moments are used for first-order gradient-based optimization of stochastic objective functions, based on adaptive estimate of lowerorder moments.

TL;DR: A new language representation model, BERT, designed to pre-train deep bidirectional representations from unlabeled text by jointly conditioning on both left and right context in all layers, which can be fine-tuned with just one additional output layer to create state-of-the-art models for a wide range of tasks.

TL;DR: A new global logbilinear regression model that combines the advantages of the two major model families in the literature: global matrix factorization and local context window methods and produces a vector space with meaningful substructure.