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Enterprise E-Mail Classification Using Instruction-Following Large Language Models
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DOI：10.3390/app16052173.png)
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Abstract
En 中文
Enterprise e-mail corpora contain heterogeneous and domain-specific content that poses challenges for conventional supervised Natural Language Processing (NLP) approaches due to class imbalance, evolving terminology, and limited labeled data. This study examines the use of instruction-following Large Language Models (LLMs) for enterprise e-mail classification under realistic operational conditions. The study evaluates instruction-based classification and semantic enrichment derived from distributional similarity as two complementary approaches for distinguishing technical from nontechnical messages. The approaches are assessed on a large-scale enterprise e-mail corpus and validated using a manually annotated subset. The results indicate that instruction-following LLMs provide stable contextual reasoning across diverse message structures, while semantic enrichment improves coverage of previously unseen technical expressions. Overall, the study presents an applied NLP framework for enterprise e-mail classification, with attention to interpretability, scalability, and robustness in real-world organizational settings.
Keywords:
natural language processing
enterprise e-mail classification
large language models
instruction-following models
applied machine learning
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