AgriLens: Semantic Retrieval in Agricultural Texts Using Topic Modeling and Language Models
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arXiv
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| Hauptverfasser: | , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2026
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| _version_ | 1866912820139917312 |
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| author | Shakeel, Heba Ahmad, Tanvir Liyaqat, Tanya Saxena, Chandni |
| author_facet | Shakeel, Heba Ahmad, Tanvir Liyaqat, Tanya Saxena, Chandni |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_08283 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | AgriLens: Semantic Retrieval in Agricultural Texts Using Topic Modeling and Language Models Shakeel, Heba Ahmad, Tanvir Liyaqat, Tanya Saxena, Chandni Information Retrieval Machine Learning 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. |
| title | AgriLens: Semantic Retrieval in Agricultural Texts Using Topic Modeling and Language Models |
| topic | Information Retrieval Machine Learning |
| url | https://arxiv.org/abs/2601.08283 |