Research on relation extraction of named entity on social media in smart cities

Abstract

Social media make significant contribution to the evolution of smart cities. The key issue of smart cities is to develop a series of automatic methods to support smart applications. As one of the basic techniques of smart cities, the task of relation extraction of named entities on social media provides an indispensable means to construct and expand knowledge map and contributes to the utilization of information resources in smart cities. What’s more, it is conducive to improve the efficiency of network supervision. This paper proposes an automatic method to extract entity relations via deep learning techniques on a two-level neural network named Bi-CLSTM. The research conquers some challenges of relation extraction on social media. To extract entity relations on conversation scenarios, Bi-CLSTM represents texts with the strategy of “word embedding + position embedding + shortest dependency path” and extracts relations via a hybrid model of LSTM and PCNN. The nodes and networks of Bi-CLSTM are designed to adapt to the scenarios of conversation and over-sentence. To reduce the dependency on training data, distant supervised strategy is employed and a two-level attention mechanism is used to prevent noise signals. Experiments are carried out on Sina Microblog corpus, and the results show that Bi-CLSTM model makes outstanding performance.