A Boundary Determined Neural Model for Relation Extraction

Existing models extract entity relations only after two entity spans have been precisely extracted that influenced the performance of relation extraction. Compared with recognizing entity spans, because the boundary has a small granularity and a less ambiguity, it can be detected precisely and incorporated to learn better representation. Motivated by the strengths of boundary, we propose a boundary determined neural (BDN) model, which leverages boundaries as task-related cues to predict the relation labels. Our model can predict high-quality relation instance via the pairs of boundaries, which can relieve error propagation problem. Moreover, our model fuses with boundary-relevant information encoding to represent distributed representation to improve the ability of capturing semantic and dependency information, which can increase the discriminability of neural network. Experiments show that our model achieves state-of-the-art performances on ACE05 corpus

Boundary Enhanced Neural Span Classification for Nested Named Entity Recognition

Named entity recognition (NER) is a well-studied task in natural language processing. However, the widely-used sequence labeling framework is usually difficult to detect entities with nested structures. The span-based method that can easily detect nested entities in different subsequences is naturally suitable for the nested NER problem. However, previous span-based methods have two main issues. First, classifying all subsequences is computationally expensive and very inefficient at inference. Second, the span-based methods mainly focus on learning span representations but lack of explicit boundary supervision. To tackle the above two issues, we propose a boundary enhanced neural span classification model. In addition to classifying the span, we propose incorporating an additional boundary detection task to predict those words that are boundaries of entities. The two tasks are jointly trained under a multitask learning framework, which enhances the span representation with additional boundary supervision. In addition, the boundary detection model has the ability to generate high-quality candidate spans, which greatly reduces the time complexity during inference. Experiments show that our approach outperforms all existing methods and achieves 85.3, 83.9, and 78.3 scores in terms of F1 on the ACE2004, ACE2005, and GENIA datasets, respectively