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Recognizing named entities (NEs) is commonly treated as a classification problem, and a class tag for a word or an NE candidate in a sentence is predicted. In recent neural network developments, deep structures that map categorized features into continuous representations have been adopted. Using this approach, a dense space saturated with high-order abstract semantic information is unfolded, and the prediction is based on distributed feature representations. In this paper, the positions of NEs in a sentence are represented as continuous values. Then, a regression operation is introduced to regress the boundaries of NEs in a sentence. Based on boundary regression, we design a boundary regression model to support nested NE recognition. It is a multiobjective learning framework that simultaneously predicts the classification score of an NE candidate and refines its spatial location in a sentence. This model was evaluated on the ACE 2005 Chinese and English corpus and the GENIA corpus. State-of-the-art performance was experimentally demonstrated for nested NE recognition, which outperforms related works about 5% and 2% respectively. Our model has the advantage to resolve nested NEs and support boundary regression for locating NEs in a sentence. By sharing parameters for predicting and locating, this model enables more potent nonlinear function approximators to enhance model discriminability.

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