text: 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.
  @cite_19: This paper describes our system for SemEval-2010 Task 8 on multi-way classification of semantic relations between nominals. First, the type of semantic relation is classified. Then a relation type-specific classifier determines the relation direction. Classification is performed using SVM classifiers and a number of features that capture the context, semantic role affiliation, and possible pre-existing relations of the nominals. This approach achieved an F1 score of 82.19 and an accuracy of 77.92 .
summary: There are several studies for solving relation classification task. Early methods used handcrafted features through a series of NLP tools or manually designing kernels @cite_19 . These approaches use high-level lexical and syntactic features obtained from NLP tools and manually designing kernels, but the classification models relying on such features suffer from propagation of implicit error of the tools.