Shaofan Liu, Guoqiang Zhang, Shihan Dou, Huiyuan Zheng, Yiming Zhou, Junjie Ye, Shaowen Wang, Shichun Liu, Jiazheng Zhang, Tao Gui, Qi Zhang, Xuanjing Huang. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2026.

MOTIVATION: Automatic information extraction from biomedical texts requires machine learning methodology that can recognize biomedical entities, characterize inter-entity relationships, and relate extracted information to specific research topics. Large language models (LLMs) excel in general tasks but perform less reliably in the biomedical domain, where texts are characterized by extensive technical terminology and semantic variations from general literature. There is an unmet need for annotated full-text datasets that can be used to fine-tune language models for significant biomedical applications. Here, we focus on extraction of the complex relationships between genes and diseases. RESULTS: We present BioTriplex, a corpus of 100 full-length biomedical research articles (comprising 604 subsection texts) manually annotated with disease names, genes, and 21 subtypes of disease-gene relationships. We employ BioTriplex to train the LLaMA 3.1 8B language model in gene-disease relation extraction. Our fine-tuned model outperforms zero-shot and few-shot approaches, both within the LLaMA 3.1 architecture and across the larger state-of-the-art LLMs GPT-4 and Claude Sonnet 3.7, and classifies gene-disease relation types with broader scope and greater granularity than previously described. These results validate BioTriplex as a useful full-text data resource and underscore the value of specialized datasets in fine-tuning language models for important biomedical tasks. AVAILABILITY AND IMPLEMENTATION: https://github.com/PanagiotisFytas/BioTriplex.

Shihan Dou, Jiayi Chen, Chenhao Huang, Feng Chen, Wei Chengzhi, Huiyuan Zheng, Shichun Liu, Yan Liu, Chenxiao Liu, Chao Xin, Lin Yan, Zongzhang Zhang, Tao Gui, Qi Zhang, Xuanjing Huang. Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2025.

Open Named Entity Recognition (NER), which involves identifying arbitrary types of entities from arbitrary domains, remains challenging for Large Language Models (LLMs). Recent studies suggest that fine-tuning LLMs on extensive NER data can boost their performance. However, training directly on existing datasets neglects their inconsistent entity definitions and redundant data, limiting LLMs to dataset-specific learning and hindering out-of-domain adaptation. To address this, we present B2NERD, a compact dataset designed to guide LLMs’ generalization in Open NER under a universal entity taxonomy. B2NERD is refined from 54 existing English and Chinese datasets using a two-step process. First, we detect inconsistent entity definitions across datasets and clarify them by distinguishable label names to construct a universal taxonomy of 400+ entity types. Second, we address redundancy using a data pruning strategy that selects fewer samples with greater category and semantic diversity. Comprehensive evaluation shows that B2NERD significantly enhances LLMs’ Open NER capabilities. Our B2NER models, trained on B2NERD, outperform GPT-4 by 6.8-12.0 F1 points and surpass previous methods in 3 out-of-domain benchmarks across 15 datasets and 6 languages. The data, models, and code are publicly available at https://github.com/UmeanNever/B2NER.

Chenhao Huang, Ziyu Shen, Yicong Ren, Huiyuan Zheng, Jiazheng Zhang, Mingxu Chai, Ming Zhang, Shihan Dou, Fan Mo, Jie Shi, Tao Gui, Qi Zhang, Xuanjing Huang. Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing. 2025.

Ming Zhang, Caishuang Huang, Yilong Wu, Shichun Liu, Huiyuan Zheng, Yurui Dong, Yujiong Shen, Shihan Dou, Jun Zhao, Junjie Ye, Qi Zhang, Tao Gui, Xuanjing Huang. Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing. 2024.

MOTIVATION: Scientific advances build on the findings of existing research. The 2001 publication of the human genome has led to the production of huge volumes of literature exploring the context-specific functions and interactions of genes. Technology is needed to perform large-scale text mining of research papers to extract the reported actions of genes in specific experimental contexts and cell states, such as cancer, thereby facilitating the design of new therapeutic strategies. RESULTS: We present a new corpus and Text Mining methodology that can accurately identify and extract the most important details of cancer genomics experiments from biomedical texts. We build a Named Entity Recognition model that accurately extracts relevant experiment details from PubMed abstract text, and a second model that identifies the relationships between them. This system outperforms earlier models and enables the analysis of gene function in diverse and dynamically evolving experimental contexts. AVAILABILITY AND IMPLEMENTATION: Code and data are available here: https://github.com/cambridgeltl/functional-genomics-ie.

In this article, we propose a comprehensive approach for Quality of Service (QoS) calculation in service composition. Differing from the existing work on QoS aggregations that represent QoS as single values, discrete values with frequencies, or standard statistical distributions, the proposed approach has the capability to handle any type of QoS probability distribution. A set of formulae and algorithms are developed to calculate the QoS of a composite service according to four identified basic patterns as sequential, parallel, conditional, and loop. We demonstrate that the proposed QoS calculation method is much more efficient than existing simulation methods. It has a high scalability and builds a solid foundation for real-time QoS analysis and prediction in service composition. Experiment results are provided to show the effectiveness and efficiency of the proposed method.

Quality of service (QoS) is a major concern in the design and management of a composite service. In this paper, a systematic approach is proposed to calculate QoS for composite services with complex structures, taking into consideration of the probability and conditions of each execution path. Four types of basic composition patterns for composite services are discussed: sequential, parallel, loop, and conditional patterns. In particular, QoS solutions are provided for unstructured conditional and loop patterns. We also show how QoS-based service selection can be conducted based on the proposed QoS calculation. Experiments have been conducted to show the effectiveness of the proposed method.

In this paper, a QoS Distribution estimation Tool (QoSDIST) is developed to estimate the QoS distributions for service compositions. QoSDIST can generate QoS probability distributions for component web services. When estimating the QoS probability distribution for a service composition, QoSDIST does not put any constraints on the representation of the QoSs of component web services, i.e., the QoS of a component web service can be in single value, discrete values with frequencies, standard statistical distribution, or any general distribution regardless of its shape, which can not be done by any existing approaches. Moreover, QoSDIST can deal with commonly used composition patterns, including loop with arbitrary exit points.