Iterative NLP Query Refinement for Enhancing Domain-Specific Information Retrieval: A Case Study in Career Services

• URL: http://arxiv.org/abs/2412.17075v1
• Date: Sun, 22 Dec 2024 15:57:35 GMT
• Title: Iterative NLP Query Refinement for Enhancing Domain-Specific Information Retrieval: A Case Study in Career Services
• Authors: Elham Peimani, Gurpreet Singh, Nisarg Mahyavanshi, Aman Arora, Awais Shaikh,
• Abstract summary: Retrieving semantically relevant documents in niche domains poses significant challenges for TF-IDF-based systems. This paper introduces an iterative and semi-automated query refinement methodology tailored to Humber College's career services webpages.
• Score: 0.13980986259786224
• License:
• Abstract: Retrieving semantically relevant documents in niche domains poses significant challenges for traditional TF-IDF-based systems, often resulting in low similarity scores and suboptimal retrieval performance. This paper addresses these challenges by introducing an iterative and semi-automated query refinement methodology tailored to Humber College's career services webpages. Initially, generic queries related to interview preparation yield low top-document similarities (approximately 0.2--0.3). To enhance retrieval effectiveness, we implement a two-fold approach: first, domain-aware query refinement by incorporating specialized terms such as resources-online-learning, student-online-services, and career-advising; second, the integration of structured educational descriptors like "online resume and interview improvement tools." Additionally, we automate the extraction of domain-specific keywords from top-ranked documents to suggest relevant terms for query expansion. Through experiments conducted on five baseline queries, our semi-automated iterative refinement process elevates the average top similarity score from approximately 0.18 to 0.42, marking a substantial improvement in retrieval performance. The implementation details, including reproducible code and experimental setups, are made available in our GitHub repositories \url{https://github.com/Elipei88/HumberChatbotBackend} and \url{https://github.com/Nisarg851/HumberChatbot}. We also discuss the limitations of our approach and propose future directions, including the integration of advanced neural retrieval models.

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