Deep Neural Network for Chinese Address Feature Extraction
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Solution Overview
Problem
Current methods for address information processing in GIS systems face challenges such as information islands, additional data dependence, and weak generalization, limiting the effective use of address data in smart cities, due to the inability to deeply extract feature connotations from address texts.
Innovation Solution
A deep neural network model is developed for Chinese address information feature extraction, utilizing a word embedding module, multi-head self-attention mechanism, and K-Means clustering to convert address texts into semantic-geospatial fusion vectors, enabling accurate feature extraction and generalization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional unstructured text management methods are used for address coding, then the processing is simple, but the model lacks generalization ability and creates information islands
Solution Approach 1:
The pre-trained language model is designed to perform multiple functions: it can extract semantic features from address text, understand spatial relationships, and adapt to different address formats. This multi-functional approach enables the model to handle diverse address data without requiring separate processing systems for each type, thereby improving generalization ability while managing complexity through a unified architecture
Solution Approach 2:
The model undergoes pre-training on large-scale address data before being applied to specific tasks. This preliminary action allows the model to learn general patterns and features of address text in advance, so that when deployed, it can immediately generalize to new address formats and contexts without requiring extensive task-specific customization, thus improving adaptability while keeping the deployment process simple
2Measurement precision
If deep neural network models are used to extract semantic features, then the feature extraction capability is enhanced, but the data requirements and training time increase significantly
Solution Approach 1:
The language model is pre-trained on large-scale address data before being fine-tuned for specific address coding tasks. This preliminary pre-training allows the model to learn general address patterns and semantic relationships in advance, so that during the actual application phase, only minimal task-specific fine-tuning is needed. This approach achieves high feature extraction accuracy while significantly reducing the training time required at deployment, as the bulk of learning occurs during the reusable pre-training phase
Solution Approach 2:
The model employs parameter-efficient fine-tuning techniques where only a subset of parameters are updated during task-specific training, while the majority of pre-trained parameters remain fixed. This allows the model to adapt to specific address coding tasks with high accuracy while minimizing the computational resources and time required for training, as changing a small number of parameters is much faster than retraining the entire model
3Measurement precision
If high-dimensional semantic feature vectors are used to accurately express address features, then the representation accuracy improves, but the computational complexity and data volume requirements increase
Solution Approach 1:
The model transforms address data from traditional tabular or text-based representations into high-dimensional semantic feature vectors that capture nuanced relationships between address components. By adding this semantic dimension, the model can represent complex address patterns and spatial relationships that would require exponentially more data in traditional representations, thus achieving high representation accuracy without proportionally increasing data volume requirements
Solution Approach 2:
The model uses parameter-efficient techniques where the high-dimensional feature space is learned through pre-trained parameters that are reused across different tasks. Instead of requiring separate high-dimensional representations trained on large datasets for each address coding task, the same parameter set can be applied to multiple tasks after pre-training, reducing the effective data volume needed while maintaining high representation accuracy through the rich semantic features captured during pre-training
Data Source
AI summary
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.