# An Open Relation Extraction Method for Domain Text Based on Hybrid Supervised Learning
###### Abstract
Unlike the generic domain, the task of open relation extraction is simplified by the given core entities in the specialized domain. These primary entities often come from a lexicon of entities defined semi-automatically by experts, such as part names of equipment and core knowledge points of specific domain knowledge. This lexicon is obtained by experts using a large corpus to extract domain terms using mining algorithms, manual reviews, and the extraction of sub-entities and relation words or attributes, and attribute values directly under the condition of known primary entities improves the extraction effect to a certain extent. At the same time, there are many complex relation in the domain of text information. For example, in the industrial text, a graphitization furnace temperature measurement device includes a furnace body mechanism and a temperature measurement mechanism. The overlapping triadic relation of the primary entity graphitization furnace temperature measurement device include (1) a graphitization furnace temperature measurement device, including a furnace body mechanism, and (2) a graphitization furnace temperature measurement device, including a temperature measurement mechanism. The hidden triadic relation includes a graphitization furnace temperature measurement device with the function of temperature measurement. The joint extraction model proposed by [PERSON] [12] transforms the problem into a sequence labeling problem and uses a close combination approach to extract relational triples. Although this approach can extract multiple relation from sentences, it does not effectively solve the task of relational extraction in different domains. Second, to solve the complex problem of entity relation and the relation extraction task in different domains, this study proposed an inflated gate attention network structure for increasing the perceptual model field for a given principal entity and designed an open relation extraction model Ro-DGANet based on the idea of fusing probabilistic graphs [13; 14; 15] based on this structure. The model incorporates Chinese lexical information and uses an inflated convolutional neural network [16] to extract features used to enrich the input features. It is trained by adding a gating [17] mechanism of residual structure, which can better understand the semantic information. This mechanism is less likely to cause gradient disappearance and improve computational speed. The attention mechanism is also introduced to find the interrelation between word vectors and thus achieve better extraction results. The main contributions of this study are as follows:  
1. To address the problem of a lack of domain-annotated corpus, this study to improve the bootstrapping [18; 19] algorithm, combined with the remote supervision algorithm, to back-label high-quality triad samples from external knowledge bases, such as Baidu Encyclopedia. Furthermore, it refined the extraction pattern which was refined based on the syntactic structure in the existing training samples. Based on the extraction pattern from the unstructured text, triadic relation were openly labeled. A high-quality training corpus was constructed using manual review, thus solving the problem of the lack of an annotated corpus;
2. To solve the problem of complex entity relation types and overlapping relation in the professional domain, the Ro-DGANet model provides an efficient and concise open relation extraction scheme. Based on the idea of a probabilistic graph, the model solves the complex relation problem, in which multiple relation exist for the same primary entity without limiting the relation types. At the same time, it proposes an inflated gate attention network structure for increasing the field-of-view of the model to extract the semantic features of the entire sentence, which can enable the model to better understand the semantic information;
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