让语法解析更懂实体，减少关键名词被拆散的问题。
Entity-Aware Biaffine Attention Model for Improved Constituent Parsing with Reduced Entity Violations
- 在注意力机制中加入实体角色向量，引导模型关注完整实体
- 在三个数据集上实现最低实体违规率，同时保持高准确率
- 适合需要精确语义结构的下游任务，如情感分析
句法解析旨在将句子分解为子短语（即成分）。尽管许多深度神经网络模型在该任务上已达到顶尖性能，但常忽略实体违规问题——即重要实体未能在解析树中形成完整子树。为此，本文提出一种面向实体的双仿射注意力模型，在双仿射注意力机制中引入潜在短语的实体角色向量，以增强解析准确性。我们提出了新的评估指标实体违规率（EVR）来量化解析结果中的实体违规程度。在ONTONOTES、PTB和CTB三个主流数据集上的实验表明，该模型在保持与现有模型相当的精确率、召回率和F1值的同时，实现了最低的EVR。下游任务如句子情感分析的进一步评估验证了模型的有效性及所提EVR指标的合理性。
原文摘要 · Abstract (English)
Constituency parsing involves analyzing a sentence by breaking it into sub-phrases, or constituents. While many deep neural models have achieved state-of-the-art performance in this task, they often overlook the entity-violating issue, where an entity fails to form a complete sub-tree in the resultant parsing tree. To address this, we propose an entity-aware biaffine attention model for constituent parsing. This model incorporates entity information into the biaffine attention mechanism by using additional entity role vectors for potential phrases, which enhances the parsing accuracy. We introduce a new metric, the Entity Violating Rate (EVR), to quantify the extent of entity violations in parsing results. Experiments on three popular datasets-ONTONOTES, PTB, and CTB-demonstrate that our model achieves the lowest EVR while maintaining high precision, recall, and F1-scores comparable to existing models. Further evaluation in downstream tasks, such as sentence sentiment analysis, highlights the effectiveness of our model and the validity of the proposed EVR metric.
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