Design of an Automatic Help Desk Response Module Using Natural Language Processing

Manual help desk systems in enterprise environments often suffer from delayed response times and repetitive queries, reducing service efficiency. This research aims to design an automated help desk response module by applying Natural Language Processing (NLP) techniques, specifically within the asset management context of an ERP system. The module uses Term Frequency-Inverse Document Frequency (TF-IDF) and cosine similarity to classify incoming queries and retrieve relevant answers from a predefined knowledge base. Python, Django, PostgreSQL, Scikit-learn, and NLTK were used to implement the module. Testing was conducted using 50 sample queries, resulting in an accuracy of 90% based on confusion matrix evaluation. The system successfully retrieves appropriate responses for most frequent user issues. This design is expected to support organizations in streamlining their help desk operations and improving response time and consistency. Future developments may involve semantic matching and machine learning-based improvements to enhance understanding of unstructured queries.