Semantic rule-based information extraction for meteorological reports

Meteorological reports are one of the most important means of recording the weather conditions of a place over a period of time, and the existence of a large number of meteorological reports creates a huge demand for text processing and information extraction. However, valuable data and information are still buried deep in the mountain of meteorological reports, and there is an urgent need for an automated information extraction technique to help people integrate data from multiple meteorological reports and perform data analysis for a more comprehensive understanding of a specific meteorological topic or domain. Named entity recognition (NER) technique can extract useful entity information from meteorological reports. By analyzing the characteristics of nested entities in meteorological reports, this paper further proposes to introduce Multi-Conditional Random Fields (Multi-CRF), which uses each layer of CRF to output the recognition results of each type of entities, which helps to solve the problem of identifying nested entities in meteorological reports. The experimental results show that our model achieves state-of-the-art results. The final recognition results provide effective data support for automatic text verification recognition in the meteorological domain and provide important practical value for the construction of knowledge graphs of related meteorological reports.