Text Segmentation Algorithm Focused on Corpus Mining for Oilfield Exploration and Development

The professional knowledge of oilfield exploration and development is vast and complex, and manual organization is time-consuming. Inspired by natural language large models and sequential modeling, a long text segmentation algorithm focused on corpus mining for oilfield exploration and development is proposed to automate the organization of professional texts. By using sequential modeling and semantic relevance, key information from the original text is obtained. With the help of the expressive power of general large models and the cross-attention mechanism, the algorithm captures the closeness between sentences in the text. Based on semantic atomization, the algorithm automatically splits long texts and filters out irrelevant content. The results show that compared to existing deep learning methods, this approach significantly improves the accuracy of text segmentation, providing a better choice for subsequent mining of professional corpus in oilfield exploration and development.