mlsanityData to 7 Oct 2026

Improving Retrieval in Theme-specific Applications using a Corpus Topical Taxonomy

arXiv 2403.04160 ↗First posted 7 Mar 2024cs.IR cs.AIcited by 11

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Abstract (arXiv)

Document retrieval has greatly benefited from the advancements of large-scale pre-trained language models (PLMs). However, their effectiveness is often limited in theme-specific applications for specialized areas or industries, due to unique terminologies, incomplete contexts of user queries, and specialized search intents. To capture the theme-specific information and improve retrieval, we propose to use a corpus topical taxonomy, which outlines the latent topic structure of the corpus while reflecting user-interested aspects. We introduce ToTER (Topical Taxonomy Enhanced Retrieval) framework, which identifies the central topics of queries and documents with the guidance of the taxonomy, and exploits their topical relatedness to supplement missing contexts. As a plug-and-play framework, ToTER can be flexibly employed to enhance various PLM-based retrievers. Through extensive quantitative, ablative, and exploratory experiments on two real-world datasets, we ascertain the benefits of using topical taxonomy for retrieval in theme-specific applications and demonstrate the effectiveness of ToTER.

Key facts. Cited by 11 papers in the map (data through 7 Oct 2026). Builds on 45 of its 60 references that are in the map. First posted on arXiv 7 Mar 2024.

Builds on

45 of 60 references.

References · 45

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33 more
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Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 5; 2025: 5; 2026: 1.

11 papers.

  1. 2025RAS: Retrieval-And-Structuring for Knowledge-Intensive LLM Generationcited by 16
  2. 2024Robust Neural Information Retrieval: An Adversarial and Out-of-distribution Perspectivecited by 15
  3. 2024Taxonomy-guided Semantic Indexing for Academic Paper Searchcited by 12
  4. 2024CodeTaxo: Enhancing Taxonomy Expansion with Limited Examples via Code Language Promptscited by 10
  5. 2025Scientific Paper Retrieval with LLM-Guided Semantic-Based Rankingcited by 10
  6. 2025A Survey on Retrieval And Structuring Augmented Generation with Large Language Modelscited by 8
  7. 2024RRADistill: Distilling LLMs' Passage Ranking Ability for Long-Tail Queries Document Re-Ranking on a Search Enginecited by 6
  8. 2025Improving Scientific Document Retrieval with Concept Coverage-based Query Set Generationcited by 6
  9. 2025CoRank: LLM-Based Compact Reranking with Document Features for Scientific Retrievalcited by 5
  10. 2024Inferring Scientific Cross-Document Coreference and Hierarchy with Definition-Augmented Relational Reasoningcited by 2
  11. 2026Improving Scientific Document Retrieval with Academic Concept Indexcited by 0