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Entity and relation extraction is a task that combines detecting entity mentions and recognizing entities’ semantic relationships from unstructured text. We propose a hybrid neural network model to extract entities and their relationships without any handcrafted features. The hybrid neural network contains a novel bidirectional encoder-decoder LSTM module (BiLSTM-ED) for entity extraction and a CNN module for relation classification. The contextual information of entities obtained in BiLSTM-ED further pass though to CNN module to improve the relation classification. We conduct experiments on the public dataset ACE05 (Automatic Content Extraction program) to verify the effectiveness of our method. The method we proposed achieves the state-of-the-art results on entity and relation extraction task. [ABSTRACT FROM AUTHOR]

Relation extraction is to identify the relationship of two given entities in the text. It is an important step in the task of knowledge extraction. Most conventional methods for the task of relation extraction focus on designing effective handcrafted features or learning a semantic representation of the whole sentence. Sentences with the same relationship always share the similar expressions. Besides, the semantic properties of given entities can also help to distinguish some confusing relations. Based on the above observations, we propose a neural network based framework for relation classification. It can simultaneously learn the relation pattern’s information and the semantic properties of given entities. In this framework, we explore two specific models: the CNN-based model and LSTM-based model. We conduct experiments on two public datasets: the SemEval-2010 Task8 dataset and the ACE05 dataset. The proposed method achieves the state-of-the-art result without using any external information. Additionally, the experimental results also show that our approach can represent the semantic relationship of the given entities effectively. [ABSTRACT FROM AUTHOR]