@inproceedings{jiang-etal-2026-littx,
title = "{L}it{T}x: A New Treatment Relation Extraction Dataset",
author = "Jiang, Yuhang and
Nahian, Md Sultan Al and
Xu, Li Hao Richie and
Chikkanna, Rani and
Kavuluru, Ramakanth",
editor = "Piperidis, Stelios and
Bel, N{\'u}ria and
van den Heuvel, Henk and
Ide, Nancy and
Krek, Simon and
Toral, Antonio",
booktitle = "Proceedings of the Fifteenth Language Resources and Evaluation Conference",
month = may,
year = "2026",
address = "Palma de Mallorca, Spain",
publisher = "ELRA Language Resource Association",
url = "https://aclanthology.org/2026.lrec-1.105/",
doi = "10.63317/5kshomz64z55",
pages = "1352--1360",
abstract = "The interest in biomedical relation extraction (RE) continues to persist even in the LLM era owing to RE being a prominent way to build knowledge graphs, which further ground LLM applications, especially in preventing hallucinations. Therapy-disease treatment relations from scientific literature are an important type in RE as they indicate emerging therapeutic hypotheses and off-label usages being explored in the community. An automatically extracted evolving knowledge-base of such relations will be of great utility to researchers because doing it manually is not viable with the exponential growth of biomedical articles. In this paper, toward this end, we introduce a new expert-annotated dataset LitTx for identifying treatment relationships discussed in literature given the lack of such datasets in the recent past. Besides confirmed or implied positive relations, we also introduce a new ``conditional treatment'' relation type where hedging or a potential relationship is indicated. Our baseline RE models with this new dataset demonstrate promising results, while also revealing clear areas for improvement. To foster innovation and ensure replicability in the biomedical RE community, we release our dataset, code, and annotation guidelines publicly: \url{https://github.com/bionlproc/LitTx_dataset}."
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<abstract>The interest in biomedical relation extraction (RE) continues to persist even in the LLM era owing to RE being a prominent way to build knowledge graphs, which further ground LLM applications, especially in preventing hallucinations. Therapy-disease treatment relations from scientific literature are an important type in RE as they indicate emerging therapeutic hypotheses and off-label usages being explored in the community. An automatically extracted evolving knowledge-base of such relations will be of great utility to researchers because doing it manually is not viable with the exponential growth of biomedical articles. In this paper, toward this end, we introduce a new expert-annotated dataset LitTx for identifying treatment relationships discussed in literature given the lack of such datasets in the recent past. Besides confirmed or implied positive relations, we also introduce a new “conditional treatment” relation type where hedging or a potential relationship is indicated. Our baseline RE models with this new dataset demonstrate promising results, while also revealing clear areas for improvement. To foster innovation and ensure replicability in the biomedical RE community, we release our dataset, code, and annotation guidelines publicly: https://github.com/bionlproc/LitTx_dataset.</abstract>
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%0 Conference Proceedings
%T LitTx: A New Treatment Relation Extraction Dataset
%A Jiang, Yuhang
%A Nahian, Md Sultan Al
%A Xu, Li Hao Richie
%A Chikkanna, Rani
%A Kavuluru, Ramakanth
%Y Piperidis, Stelios
%Y Bel, Núria
%Y van den Heuvel, Henk
%Y Ide, Nancy
%Y Krek, Simon
%Y Toral, Antonio
%S Proceedings of the Fifteenth Language Resources and Evaluation Conference
%D 2026
%8 May
%I ELRA Language Resource Association
%C Palma de Mallorca, Spain
%F jiang-etal-2026-littx
%X The interest in biomedical relation extraction (RE) continues to persist even in the LLM era owing to RE being a prominent way to build knowledge graphs, which further ground LLM applications, especially in preventing hallucinations. Therapy-disease treatment relations from scientific literature are an important type in RE as they indicate emerging therapeutic hypotheses and off-label usages being explored in the community. An automatically extracted evolving knowledge-base of such relations will be of great utility to researchers because doing it manually is not viable with the exponential growth of biomedical articles. In this paper, toward this end, we introduce a new expert-annotated dataset LitTx for identifying treatment relationships discussed in literature given the lack of such datasets in the recent past. Besides confirmed or implied positive relations, we also introduce a new “conditional treatment” relation type where hedging or a potential relationship is indicated. Our baseline RE models with this new dataset demonstrate promising results, while also revealing clear areas for improvement. To foster innovation and ensure replicability in the biomedical RE community, we release our dataset, code, and annotation guidelines publicly: https://github.com/bionlproc/LitTx_dataset.
%R 10.63317/5kshomz64z55
%U https://aclanthology.org/2026.lrec-1.105/
%U https://doi.org/10.63317/5kshomz64z55
%P 1352-1360
Markdown (Informal)
[LitTx: A New Treatment Relation Extraction Dataset](https://aclanthology.org/2026.lrec-1.105/) (Jiang et al., LREC 2026)
ACL
- Yuhang Jiang, Md Sultan Al Nahian, Li Hao Richie Xu, Rani Chikkanna, and Ramakanth Kavuluru. 2026. LitTx: A New Treatment Relation Extraction Dataset. In Proceedings of the Fifteenth Language Resources and Evaluation Conference, pages 1352–1360, Palma de Mallorca, Spain. ELRA Language Resource Association.