Named Entity Recognition(NER)stands as a fundamental task within the field of biomedical text mining,aiming to extract specific types of entities such as genes,proteins,and diseases from complex biomedical texts and c...Named Entity Recognition(NER)stands as a fundamental task within the field of biomedical text mining,aiming to extract specific types of entities such as genes,proteins,and diseases from complex biomedical texts and categorize them into predefined entity types.This process can provide basic support for the automatic construction of knowledge bases.In contrast to general texts,biomedical texts frequently contain numerous nested entities and local dependencies among these entities,presenting significant challenges to prevailing NER models.To address these issues,we propose a novel Chinese nested biomedical NER model based on RoBERTa and Global Pointer(RoBGP).Our model initially utilizes the RoBERTa-wwm-ext-large pretrained language model to dynamically generate word-level initial vectors.It then incorporates a Bidirectional Long Short-Term Memory network for capturing bidirectional semantic information,effectively addressing the issue of long-distance dependencies.Furthermore,the Global Pointer model is employed to comprehensively recognize all nested entities in the text.We conduct extensive experiments on the Chinese medical dataset CMeEE and the results demonstrate the superior performance of RoBGP over several baseline models.This research confirms the effectiveness of RoBGP in Chinese biomedical NER,providing reliable technical support for biomedical information extraction and knowledge base construction.展开更多
Few-shot named entity recognition(NER)aims to identify named entities in new domains using a limited amount of annotated data.Previous methods divided this task into entity span detection and entity classification,ach...Few-shot named entity recognition(NER)aims to identify named entities in new domains using a limited amount of annotated data.Previous methods divided this task into entity span detection and entity classification,achieving good results.However these methods are limited by the imbalance between the entity and non-entity categories due to the use of sequence labeling for entity span detection.To this end,a point-proto network(PPN)combining pointer and prototypical networks was proposed.Specifically,the pointer network generates the position of entities in sentences in the entity span detection stage.The prototypical network builds semantic prototypes of entity types and classifies entities based on their distance from these prototypes in the entity classification stage.Moreover,the low-rank adaptation(LoRA)fine-tuning method,which involves freezing the pre-trained weights and injecting a trainable decomposition matrix,reduces the parameters that need to be trained and saved.Extensive experiments on the few-shot NER Dataset(Few-NERD)and Cross-Dataset demonstrate the superiority of PPN in this domain.展开更多
针对农业病害领域命名实体识别过程中存在的预训练语言模型利用不充分、外部知识注入利用率低、嵌套命名实体识别率低的问题,本文提出基于连续提示注入和指针网络的命名实体识别模型CP-MRC(Continuous prompts for machine reading comp...针对农业病害领域命名实体识别过程中存在的预训练语言模型利用不充分、外部知识注入利用率低、嵌套命名实体识别率低的问题,本文提出基于连续提示注入和指针网络的命名实体识别模型CP-MRC(Continuous prompts for machine reading comprehension)。该模型引入BERT(Bidirectional encoder representation from transformers)预训练模型,通过冻结BERT模型原有参数,保留其在预训练阶段获取到的文本表征能力;为了增强模型对领域数据的适用性,在每层Transformer中插入连续可训练提示向量;为提高嵌套命名实体识别的准确性,采用指针网络抽取实体序列。在自建农业病害数据集上开展了对比实验,该数据集包含2933条文本语料,8个实体类型,共10414个实体。实验结果显示,CP-MRC模型的精确率、召回率、F1值达到83.55%、81.4%、82.4%,优于其他模型;在病原、作物两类嵌套实体的识别率较其他模型F1值提升3个百分点和13个百分点,嵌套实体识别率明显提升。本文提出的模型仅采用少量可训练参数仍然具备良好识别性能,为较大规模预训练模型在信息抽取任务上的应用提供了思路。展开更多
为获得结构化的小麦品种表型和遗传描述,针对非结构化小麦种质数据中存在的实体边界模糊以及关系重叠问题,提出一种基于深度字词融合的小麦种质信息实体关系联合抽取模型WGIE-DCWF(wheat germplasm information extraction model based ...为获得结构化的小麦品种表型和遗传描述,针对非结构化小麦种质数据中存在的实体边界模糊以及关系重叠问题,提出一种基于深度字词融合的小麦种质信息实体关系联合抽取模型WGIE-DCWF(wheat germplasm information extraction model based on deep character and word fusion)。模型编码层通过深度字词融合和上下文语义特征融合,提高密集实体特征识别能力;模型三元组抽取层建立层叠指针网络,提高重叠关系的提取能力。在小麦种质数据集和公开数据集上的一系列对比实验结果表明,WGIE-DCWF模型能够有效提高小麦种质数据实体关系联合抽取效果,同时拥有较好的泛化性,可以为小麦种质信息知识库构建提供技术支撑。展开更多
Pointer analysis is a technique to identify at compile-time the potential values of the pointer expressions in a program, which promises significant benefits for optimizing and parallelizing compilers. In this paper,...Pointer analysis is a technique to identify at compile-time the potential values of the pointer expressions in a program, which promises significant benefits for optimizing and parallelizing compilers. In this paper, a new approach to pointer analysis for assignments is presented. In this approach, assignments are classified into three categories: pointer assignments, structure (union) assignments and normal assignments which don't affect the point-to information. Pointer analyses for these three kinds of assignments respectively make up the integrated algorithm. When analyzing a pointer assignment, a new method called expression expansion is used to calculate both the left targets and the right targets. The integration of recursive data structure analysis into pointer analysis is a significant originality of this paper, which uniforms the pointer analysis for heap variables and the pointer analysis for stack variables. This algorithm is implemented in Agassiz, an analyzing tool for C programs developed by institute of Parallel Processing, Fudan University. Its accuracy and effectiveness are illustrated by experimental data.展开更多
基金supported by the Outstanding Youth Team Project of Central Universities(QNTD202308)the Ant Group through CCF-Ant Research Fund(CCF-AFSG 769498 RF20220214).
文摘Named Entity Recognition(NER)stands as a fundamental task within the field of biomedical text mining,aiming to extract specific types of entities such as genes,proteins,and diseases from complex biomedical texts and categorize them into predefined entity types.This process can provide basic support for the automatic construction of knowledge bases.In contrast to general texts,biomedical texts frequently contain numerous nested entities and local dependencies among these entities,presenting significant challenges to prevailing NER models.To address these issues,we propose a novel Chinese nested biomedical NER model based on RoBERTa and Global Pointer(RoBGP).Our model initially utilizes the RoBERTa-wwm-ext-large pretrained language model to dynamically generate word-level initial vectors.It then incorporates a Bidirectional Long Short-Term Memory network for capturing bidirectional semantic information,effectively addressing the issue of long-distance dependencies.Furthermore,the Global Pointer model is employed to comprehensively recognize all nested entities in the text.We conduct extensive experiments on the Chinese medical dataset CMeEE and the results demonstrate the superior performance of RoBGP over several baseline models.This research confirms the effectiveness of RoBGP in Chinese biomedical NER,providing reliable technical support for biomedical information extraction and knowledge base construction.
基金supported by the National Key Research and Development Project(2021YFF0901701)。
文摘Few-shot named entity recognition(NER)aims to identify named entities in new domains using a limited amount of annotated data.Previous methods divided this task into entity span detection and entity classification,achieving good results.However these methods are limited by the imbalance between the entity and non-entity categories due to the use of sequence labeling for entity span detection.To this end,a point-proto network(PPN)combining pointer and prototypical networks was proposed.Specifically,the pointer network generates the position of entities in sentences in the entity span detection stage.The prototypical network builds semantic prototypes of entity types and classifies entities based on their distance from these prototypes in the entity classification stage.Moreover,the low-rank adaptation(LoRA)fine-tuning method,which involves freezing the pre-trained weights and injecting a trainable decomposition matrix,reduces the parameters that need to be trained and saved.Extensive experiments on the few-shot NER Dataset(Few-NERD)and Cross-Dataset demonstrate the superiority of PPN in this domain.
文摘针对农业病害领域命名实体识别过程中存在的预训练语言模型利用不充分、外部知识注入利用率低、嵌套命名实体识别率低的问题,本文提出基于连续提示注入和指针网络的命名实体识别模型CP-MRC(Continuous prompts for machine reading comprehension)。该模型引入BERT(Bidirectional encoder representation from transformers)预训练模型,通过冻结BERT模型原有参数,保留其在预训练阶段获取到的文本表征能力;为了增强模型对领域数据的适用性,在每层Transformer中插入连续可训练提示向量;为提高嵌套命名实体识别的准确性,采用指针网络抽取实体序列。在自建农业病害数据集上开展了对比实验,该数据集包含2933条文本语料,8个实体类型,共10414个实体。实验结果显示,CP-MRC模型的精确率、召回率、F1值达到83.55%、81.4%、82.4%,优于其他模型;在病原、作物两类嵌套实体的识别率较其他模型F1值提升3个百分点和13个百分点,嵌套实体识别率明显提升。本文提出的模型仅采用少量可训练参数仍然具备良好识别性能,为较大规模预训练模型在信息抽取任务上的应用提供了思路。
文摘为获得结构化的小麦品种表型和遗传描述,针对非结构化小麦种质数据中存在的实体边界模糊以及关系重叠问题,提出一种基于深度字词融合的小麦种质信息实体关系联合抽取模型WGIE-DCWF(wheat germplasm information extraction model based on deep character and word fusion)。模型编码层通过深度字词融合和上下文语义特征融合,提高密集实体特征识别能力;模型三元组抽取层建立层叠指针网络,提高重叠关系的提取能力。在小麦种质数据集和公开数据集上的一系列对比实验结果表明,WGIE-DCWF模型能够有效提高小麦种质数据实体关系联合抽取效果,同时拥有较好的泛化性,可以为小麦种质信息知识库构建提供技术支撑。
基金the National Natural Science Foundation of China under grant No.69903003,Defence Science and Technology Key Laboratory Foundat
文摘Pointer analysis is a technique to identify at compile-time the potential values of the pointer expressions in a program, which promises significant benefits for optimizing and parallelizing compilers. In this paper, a new approach to pointer analysis for assignments is presented. In this approach, assignments are classified into three categories: pointer assignments, structure (union) assignments and normal assignments which don't affect the point-to information. Pointer analyses for these three kinds of assignments respectively make up the integrated algorithm. When analyzing a pointer assignment, a new method called expression expansion is used to calculate both the left targets and the right targets. The integration of recursive data structure analysis into pointer analysis is a significant originality of this paper, which uniforms the pointer analysis for heap variables and the pointer analysis for stack variables. This algorithm is implemented in Agassiz, an analyzing tool for C programs developed by institute of Parallel Processing, Fudan University. Its accuracy and effectiveness are illustrated by experimental data.
