"从中文文本中准确识别医学命名实体是实现中文医疗信息结构化的关键。传统机器学习方法在面对中文医学实体边界模糊和嵌套结构复杂等问题时效果有限。本文提出一种基于大语言模型的中文医学命名实体识别方法,首先通过任务重构将识别过程转化为文本生成任务,设计了适配的标注策略以统一处理平面与嵌套实体,然后引入实体筛选器过滤错误候选实体,最后通过大语言模型决策进行冲突消解与多模型集成提升系统整体鲁棒性。在CMeEE-V2与CCKS2019两个数据集上实验结果显示,所提方法在识别准确性与鲁棒性方面均达到当前先进水平,F1值分别为0.7785和0.8821。"

"With the widespread adoption of Electronic Medical Records (EMRs), automated coding of theInternational Classification of Diseases (ICD) has become increasingly essential. However, the complexity of Chinese clinical texts presents significant challenges to traditional methods. To address these issues, CCL25-Eval Task 8 organized the Chinese EMRs ICD Diagnosis CodingEvaluation. This paper presents a method based on Large Language Models (LLMs), which divides the task into primary and other diagnosis coding. For the primary diagnosis, a confidence-guided semantic retrieval strategy is applied, while ensemble learning enhanced with NamedEntity Recognition (NER) is used for other diagnoses. The proposed approach achieved 83.42%accuracy on the official test set, ranking second in the evaluation."

"本文面向CCL2025-Eval任务9中的中医辨证辨病与中药处方推荐两个子任务,提出了一套基于大语言模型的系统性方法。在子任务1中,本文基于QLoRA方法对Qwen2.5-7B、Mistral-7B和Baichuan-7B三种预训练模型进行高效微调,并引入多模型集成投票策略。在子任务串中,本文设计了融合向量检索、监督微调与强化学习的中药推荐框架,通过相似度检索构建候选处方集合,并利用强化学习优化模型的生成能力。最终在评测中获得总分0.5171(Task1得分0.5710,Task2得分0.4632),排名第四,验证了所提方法的有效性与实用性。"

Few-Shot Document-Level Relation Extraction (FSDLRE) aims to develop models capable of generalizing to new categories with minimal support examples. Although Large Language Models (LLMs) demonstrate exceptional In-Context Learning (ICL) capabilities on many few-shot tasks, their performance on FSDLRE tasks remains suboptimal due to the significant gap between the task format and the intrinsic capabilities of language models, coupled with the complexity of ICL prompts for document-level text. To address these challenges, we introduce a novel meta-training approach for LLMs termed Prototype Tuning. We construct simulated episodes using data with relation types that do not overlap with the test corpus, fundamentally enhancing the ICL capabilities of LLMs in FSDLRE through meta-learning. To further enhance the effects of meta-learning, we innovatively integrate the concept of prototype into the fine-tuning process of LLMs. This involves aggregating entity pairs from support documents into prototypes within the prompts and altering the way of determining relation categories to identifying the closest prototype. Experimental results demonstrate that our LLMs trained with this approach outperform all baselines. Our proposed approach markedly improves the ICL capabilities of LLMs in FSDLRE and mitigates the impact of relation semantic discrepancies between the training corpus and the test corpus on model performance.

In this work, we tackle the challenge of multi-label emotion classification, where a sentence can simultaneously express multiple emotions. This task is particularly difficult due to the overlapping nature of emotions and the limited context available in short texts. To address these challenges, we propose an ensemble approach that integrates Pre-trained Language Models (BERT-based models) and Large Language Models, each capturing distinct emotional cues within the text. The predictions from these models are aggregated through a voting mechanism, enhancing classification accuracy. Additionally, we incorporate threshold optimization and class weighting techniques to mitigate class imbalance. Our method demonstrates substantial improvements over baseline models. Our approach ranked 4th out of 90 on the English leaderboard and exhibited strong performance in English in SemEval-2025 Task 11 Track A.

We propose a multilingual text processing framework that combines multilingual translation with data augmentation, QLoRA-based multi-model fine-tuning, and GLM-4-Plus-based ensemble classification. By using GLM-4-Plus to translate multilingual texts into English, we enhance data diversity and quantity. Data augmentation effectively improves the model’s performance on imbalanced datasets. QLoRA fine-tuning optimizes the model and reduces classification loss. GLM-4-Plus, as a meta-classifier, further enhances system performance. Our system achieved first place in three languages (English, Portuguese and Russian).

“从文本中自动识别人类表型概念对疾病分析具有重大意义。现存本体驱动的表型概念识别方法主要利用本体中概念名和同义词信息,并未充分考虑本体丰富信息。针对此问题,本文提出一种基于本体信息增强的人类表型概念识别方法,利用先进大语言模型进行数据增强,并设计本体向量增强的深度学习模型来提升概念识别性能。在GSC+和ID-68两个数据集上进行实验,结果表明本文提出方法能够利用本体丰富信息有效提升基线模型性能,取得了先进结果。”

In recent years, with the vast and rapidly increasing amounts of spoken and textual data, Named Entity Recognition (NER) tasks have evolved into three distinct categories, i.e., text-based NER (TNER), Speech NER (SNER) and Multimodal NER (MNER). However, existing approaches typically require designing separate models for each task, overlooking the potential connections between tasks and limiting the versatility of NER methods. To mitigate these limitations, we introduce a new task named Integrated Multimodal NER (IMNER) to break the boundaries between different modal NER tasks, enabling a unified implementation of them. To achieve this, we first design a unified data format for inputs from different modalities. Then, leveraging the pre-trained MMSpeech model as the backbone, we propose an **I**ntegrated **M**ultimod**a**l **Ge**neration Framework (**IMAGE**), formulating the Chinese IMNER task as an entity-aware text generation task. Experimental results demonstrate the feasibility of our proposed IMAGE framework in the IMNER task. Our work in integrated multimodal learning in advancing the performance of NER may set up a new direction for future research in the field. Our source code is available at https://github.com/NingJinzhong/IMAGE4IMNER.

In this paper, we formulate the personalized news headline generation problem whose goal is to output a user-specific title based on both a user’s reading interests and a candidate news body to be exposed to her. To build up a benchmark for this problem, we publicize a large-scale dataset named PENS (PErsonalized News headlineS). The training set is collected from user impressions logs of Microsoft News, and the test set is manually created by hundreds of native speakers to enable a fair testbed for evaluating models in an offline mode. We propose a generic framework as a preparatory solution to our problem. At its heart, user preference is learned by leveraging the user behavioral data, and three kinds of user preference injections are proposed to personalize a text generator and establish personalized headlines. We investigate our dataset by implementing several state-of-the-art user modeling methods in our framework to demonstrate a benchmark score for the proposed dataset. The dataset is available at https://msnews.github.io/pens.html.

The pivot for the unified Aspect-based Sentiment Analysis (ABSA) is to couple aspect terms with their corresponding opinion terms, which might further derive easier sentiment predictions. In this paper, we investigate the unified ABSA task from the perspective of Machine Reading Comprehension (MRC) by observing that the aspect and the opinion terms can serve as the query and answer in MRC interchangeably. We propose a new paradigm named Role Flipped Machine Reading Comprehension (RF-MRC) to resolve. At its heart, the predicted results of either the Aspect Term Extraction (ATE) or the Opinion Terms Extraction (OTE) are regarded as the queries, respectively, and the matched opinion or aspect terms are considered as answers. The queries and answers can be flipped for multi-hop detection. Finally, every matched aspect-opinion pair is predicted by the sentiment classifier. RF-MRC can solve the ABSA task without any additional data annotation or transformation. Experiments on three widely used benchmarks and a challenging dataset demonstrate the superiority of the proposed framework.

Multi-source neural machine translation aims to translate from parallel sources of information (e.g. languages, images, etc.) to a single target language, which has shown better performance than most one-to-one systems. Despite the remarkable success of existing models, they usually neglect the fact that multiple source inputs may have inconsistencies. Such differences might bring noise to the task and limit the performance of existing multi-source NMT approaches due to their indiscriminate usage of input sources for target word predictions. In this paper, we attempt to leverage the potential complementary information among distinct sources and alleviate the occasional conflicts of them. To accomplish that, we propose a source invariance network to learn the invariant information of parallel sources. Such network can be easily integrated with multi-encoder based multi-source NMT methods (e.g. multi-encoder RNN and transformer) to enhance the translation results. Extensive experiments on two multi-source translation tasks demonstrate that the proposed approach not only achieves clear gains in translation quality but also captures implicit invariance between different sources.

In this work, we re-examine the problem of extractive text summarization for long documents. We observe that the process of extracting summarization of human can be divided into two stages: 1) a rough reading stage to look for sketched information, and 2) a subsequent careful reading stage to select key sentences to form the summary. By simulating such a two-stage process, we propose a novel approach for extractive summarization. We formulate the problem as a contextual-bandit problem and solve it with policy gradient. We adopt a convolutional neural network to encode gist of paragraphs for rough reading, and a decision making policy with an adapted termination mechanism for careful reading. Experiments on the CNN and DailyMail datasets show that our proposed method can provide high-quality summaries with varied length, and significantly outperform the state-of-the-art extractive methods in terms of ROUGE metrics.