Address information feature extraction method based on deep neural network model

Abstract

The present disclosure discloses an address information feature extraction method based on a deep neural network model. The present disclosure uses a deep neural network architecture, and transforms tasks, such as text feature extraction, address standardization construction and semantic-geospatial fusion, into quantifiable deep neural network model construction and training optimization problems. Taking a character in an address as a basic input unit, the address language model is designed to express it in vectors, then a key technology of standardization construction of Chinese addresses is realized through neural network target tasks. Moreover, considering expression features of the Chinese addresses and geospatial attributes, a fusion scheme of address semantic representations and geographical coordinates is proposed, and a weighted clustering method and a feature fusion model are designed, to extract, from an address text of a natural language, a fusion vector syncretic with semantic features and spatial features.