How to efficiently query large amounts of personnel data through natural language processing?

Efficient search of massive personnel data: the application of natural language processing

In large personnel databases, how to use natural language processing (NLP) technology to achieve efficient query is a key challenge. For example, you want to quickly find information on people who meet the age (0-25 years old), work location (Beijing) and gender (male) conditions by typing natural language statements like "Male under 25 years old". Suppose your data is stored in MySQL or ElasticSearch and developed based on the Java SpringBoot framework.

You may have tried several methods, but the effect is not ideal: you directly call the OpenAI interface, vectorize the personnel data and search in ElasticSearch; use HanLP to segment words and convert properties; and try Stanford NLP to segment words. These methods perform well in simple queries, but under complex queries, accuracy and efficiency are limited.

Based on this, vectorizing personnel data and using ElasticSearch for dot product search is still a feasible solution. Although there may be shortcomings when processing complex queries, by continuously optimizing parameters and models, the accuracy and speed of queries can be effectively improved. This requires refined adjustments to vectorization strategies, similarity calculation methods, and ElasticSearch's indexing strategies.

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