How to use natural language processing technology to efficiently query personnel data?

Natural language processing empowers efficient data query for personnel

It is crucial for enterprises to efficiently manage and query personnel data. This article discusses how to use natural language processing (NLP) technology to simplify the process of personnel data query. Suppose we have an employee database containing information such as age, workplace, gender, etc., and the goal is to directly retrieve matching employee information through natural language input (for example: "Men under 25 years old, working in Beijing"). This project is based on the Java SpringBoot framework and uses MySQL and ElasticSearch as data storage and retrieval engines.

Explore multiple solutions to ultimately lock in best practices

During the implementation process, we tried various NLP methods, but the effects vary:

1. OpenAI vectorized ElasticSearch dot product query: convert employee data into vector representation, and use ElasticSearch to search vector similarity. Although it is theoretically feasible, the actual effect is limited by the accuracy of vector representation and query efficiency.

2. HanLP word participle attribute conversion: Use HanLP for natural language word segmentation, and then convert the word participle result into attribute conditions that can be used for database query. However, when HanLP handles complex query statements, the word segmentation accuracy is insufficient, which leads to difficulty in property conversion.

3. StanfordNLP word segmentation: Similar to HanLP, StanfordNLP also has shortcomings in terms of word segmentation accuracy of complex query statements, making it difficult to effectively extract keywords.

The best solution after optimization: the perfect combination of OpenAI vectorization and ElasticSearch

After repeated testing and optimization, we found that the solution based on OpenAI vectorization and ElasticSearch dot product query finally achieved the best results after parameter adjustment and model optimization.

By converting both natural language query and employee data into vector representations, and using ElasticSearch's vector similarity search function, we achieve efficient and accurate retrieval of personnel data. This solution significantly improves query efficiency and accuracy, becoming the most ideal solution at present.

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