Classifying semantic relations between entity pairs in sentences is an
important task in Natural Language Processing (NLP). Most previous models for
relation classification rely on the high-level lexical and syntactic features
obtained by NLP tools such as WordNet, dependency parser, part-of-speech (POS)
tagger, and named entity recognizers (NER). In addition, state-of-the-art
neural models based on attention mechanisms do not fully utilize information of
entity that may be the most crucial features for relation classification. To
address these issues, we propose a novel end-to-end recurrent neural model
which incorporates an entity-aware attention mechanism with a latent entity
typing (LET) method. Our model not only utilizes entities and their latent
types as features effectively but also is more interpretable by visualizing
attention mechanisms applied to our model and results of LET. Experimental
results on the SemEval-2010 Task 8, one of the most popular relation
classification task, demonstrate that our model outperforms existing
state-of-the-art models without any high-level features.

本文提出了一种新颖的端到端循环神经模型，结合了实体感知的注意力机制和潜在实体类型（LET）方法，以解决语义关系分类任务中现有模型对高级语义和句法特征的过度依赖和未充分利用实体信息等问题。在 SemEval-2010 任务 8 上的实验结果表明，该模型在无需高级特征的情况下胜过现有最先进的模型。