Object Recognition Base on Deep Belief Network

Event ontology is a general knowledge base constructed by event as the basic knowledge unit for computer communication. Event contains six elements which are action, object, time, environment, assertion and language performance. In this paper, we mainly discuss object elements recognition. There are several mainly existing way to recognize object: methods based on rule, statistical and shallow machine learning. Although these methods can get better recognition results in a particular environment, they have nature defects. For instance, it is difficult for them to do feature extraction and they can not achieve complex function approximation, leading to low recognition accuracy and scalability. Aiming at problems of existing object recognition methods, we present a Chinese emergency object recognition model based on deep learning (CEORM). Firstly, we use word segmentation system (LTP) to segment sentence, and classify words according to annotating elements in CEC2.0 corpus, and then obtain each word's vectorization of multiple features, which include part of speech, dependency grammar, length, location. We obtain word's deep semantic characteristics from the collection after vectorization using deep belief network, finally, object elements are classified and recognized. Extensive testing analysis shows that our proposed method can achieve better recognition effect.