AgriLens: Semantic Retrieval in Agricultural Texts Using Topic Modeling and Language Models

• URL: http://arxiv.org/abs/2601.08283v1
• Date: Tue, 13 Jan 2026 07:18:59 GMT
• Title: AgriLens: Semantic Retrieval in Agricultural Texts Using Topic Modeling and Language Models
• Authors: Heba Shakeel, Tanvir Ahmad, Tanya Liyaqat, Chandni Saxena,
• Abstract summary: This work presents a unified framework for interpretable topic modeling, zero-shot topic labeling, and topic-guided semantic retrieval over large agricultural text corpora.
• Score: 1.0345929832241805
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: As the volume of unstructured text continues to grow across domains, there is an urgent need for scalable methods that enable interpretable organization, summarization, and retrieval of information. This work presents a unified framework for interpretable topic modeling, zero-shot topic labeling, and topic-guided semantic retrieval over large agricultural text corpora. Leveraging BERTopic, we extract semantically coherent topics. Each topic is converted into a structured prompt, enabling a language model to generate meaningful topic labels and summaries in a zero-shot manner. Querying and document exploration are supported via dense embeddings and vector search, while a dedicated evaluation module assesses topical coherence and bias. This framework supports scalable and interpretable information access in specialized domains where labeled data is limited.

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