@inproceedings{kang-etal-2024-taxonomy,
title = "Taxonomy-guided Semantic Indexing for Academic Paper Search",
author = "Kang, SeongKu and
Zhang, Yunyi and
Jiang, Pengcheng and
Lee, Dongha and
Han, Jiawei and
Yu, Hwanjo",
editor = "Al-Onaizan, Yaser and
Bansal, Mohit and
Chen, Yun-Nung",
booktitle = "Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2024",
address = "Miami, Florida, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.emnlp-main.407/",
doi = "10.18653/v1/2024.emnlp-main.407",
pages = "7169--7184",
abstract = "Academic paper search is an essential task for efficient literature discovery and scientific advancement. While dense retrieval has advanced various ad-hoc searches, it often struggles to match the underlying academic concepts between queries and documents, which is critical for paper search. To enable effective academic concept matching for paper search, we propose Taxonomy-guided Semantic Indexing (TaxoIndex) framework. TaxoIndex extracts key concepts from papers and organizes them as a semantic index guided by an academic taxonomy, and then leverages this index as foundational knowledge to identify academic concepts and link queries and documents. As a plug-and-play framework, TaxoIndex can be flexibly employed to enhance existing dense retrievers. Extensive experiments show that TaxoIndex brings significant improvements, even with highly limited training data, and greatly enhances interpretability."
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<abstract>Academic paper search is an essential task for efficient literature discovery and scientific advancement. While dense retrieval has advanced various ad-hoc searches, it often struggles to match the underlying academic concepts between queries and documents, which is critical for paper search. To enable effective academic concept matching for paper search, we propose Taxonomy-guided Semantic Indexing (TaxoIndex) framework. TaxoIndex extracts key concepts from papers and organizes them as a semantic index guided by an academic taxonomy, and then leverages this index as foundational knowledge to identify academic concepts and link queries and documents. As a plug-and-play framework, TaxoIndex can be flexibly employed to enhance existing dense retrievers. Extensive experiments show that TaxoIndex brings significant improvements, even with highly limited training data, and greatly enhances interpretability.</abstract>
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%0 Conference Proceedings
%T Taxonomy-guided Semantic Indexing for Academic Paper Search
%A Kang, SeongKu
%A Zhang, Yunyi
%A Jiang, Pengcheng
%A Lee, Dongha
%A Han, Jiawei
%A Yu, Hwanjo
%Y Al-Onaizan, Yaser
%Y Bansal, Mohit
%Y Chen, Yun-Nung
%S Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing
%D 2024
%8 November
%I Association for Computational Linguistics
%C Miami, Florida, USA
%F kang-etal-2024-taxonomy
%X Academic paper search is an essential task for efficient literature discovery and scientific advancement. While dense retrieval has advanced various ad-hoc searches, it often struggles to match the underlying academic concepts between queries and documents, which is critical for paper search. To enable effective academic concept matching for paper search, we propose Taxonomy-guided Semantic Indexing (TaxoIndex) framework. TaxoIndex extracts key concepts from papers and organizes them as a semantic index guided by an academic taxonomy, and then leverages this index as foundational knowledge to identify academic concepts and link queries and documents. As a plug-and-play framework, TaxoIndex can be flexibly employed to enhance existing dense retrievers. Extensive experiments show that TaxoIndex brings significant improvements, even with highly limited training data, and greatly enhances interpretability.
%R 10.18653/v1/2024.emnlp-main.407
%U https://aclanthology.org/2024.emnlp-main.407/
%U https://doi.org/10.18653/v1/2024.emnlp-main.407
%P 7169-7184
Markdown (Informal)
[Taxonomy-guided Semantic Indexing for Academic Paper Search](https://aclanthology.org/2024.emnlp-main.407/) (Kang et al., EMNLP 2024)
ACL
- SeongKu Kang, Yunyi Zhang, Pengcheng Jiang, Dongha Lee, Jiawei Han, and Hwanjo Yu. 2024. Taxonomy-guided Semantic Indexing for Academic Paper Search. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, pages 7169–7184, Miami, Florida, USA. Association for Computational Linguistics.