Ilaria Bartolini , Vincenzo Moscato , Marco Postiglione , Giancarlo Sperlì , Andrea Vignali
{"title":"基于上下文相似性的数据增强:在生物医学命名实体识别中的应用","authors":"Ilaria Bartolini , Vincenzo Moscato , Marco Postiglione , Giancarlo Sperlì , Andrea Vignali","doi":"10.1016/j.is.2023.102291","DOIUrl":null,"url":null,"abstract":"<div><p>In this paper, we present COntext SImilarity-based data augmentation for NER (COSINER), a new method for improving Named Entity Recognition (NER) tasks using data augmentation. Unlike current techniques, which may generate noisy and mislabeled samples through text manipulation, COSINER uses context similarity to replace entity mentions with more plausible ones on the basis of available training data and considering the context in which entities typically appear. Through experiments on popular benchmark datasets, we show that COSINER outperforms existing baselines in various few-shot scenarios where training data is limited. Additionally, our method’s computing times are comparable to the simplest augmentation methods and are better than approaches that rely on pre-trained models in their architecture.</p></div>","PeriodicalId":50363,"journal":{"name":"Information Systems","volume":null,"pages":null},"PeriodicalIF":3.0000,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Data augmentation via context similarity: An application to biomedical Named Entity Recognition\",\"authors\":\"Ilaria Bartolini , Vincenzo Moscato , Marco Postiglione , Giancarlo Sperlì , Andrea Vignali\",\"doi\":\"10.1016/j.is.2023.102291\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>In this paper, we present COntext SImilarity-based data augmentation for NER (COSINER), a new method for improving Named Entity Recognition (NER) tasks using data augmentation. Unlike current techniques, which may generate noisy and mislabeled samples through text manipulation, COSINER uses context similarity to replace entity mentions with more plausible ones on the basis of available training data and considering the context in which entities typically appear. Through experiments on popular benchmark datasets, we show that COSINER outperforms existing baselines in various few-shot scenarios where training data is limited. Additionally, our method’s computing times are comparable to the simplest augmentation methods and are better than approaches that rely on pre-trained models in their architecture.</p></div>\",\"PeriodicalId\":50363,\"journal\":{\"name\":\"Information Systems\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":3.0000,\"publicationDate\":\"2023-10-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Information Systems\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0306437923001278\",\"RegionNum\":2,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"COMPUTER SCIENCE, INFORMATION SYSTEMS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Information Systems","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0306437923001278","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
Data augmentation via context similarity: An application to biomedical Named Entity Recognition
In this paper, we present COntext SImilarity-based data augmentation for NER (COSINER), a new method for improving Named Entity Recognition (NER) tasks using data augmentation. Unlike current techniques, which may generate noisy and mislabeled samples through text manipulation, COSINER uses context similarity to replace entity mentions with more plausible ones on the basis of available training data and considering the context in which entities typically appear. Through experiments on popular benchmark datasets, we show that COSINER outperforms existing baselines in various few-shot scenarios where training data is limited. Additionally, our method’s computing times are comparable to the simplest augmentation methods and are better than approaches that rely on pre-trained models in their architecture.
期刊介绍:
Information systems are the software and hardware systems that support data-intensive applications. The journal Information Systems publishes articles concerning the design and implementation of languages, data models, process models, algorithms, software and hardware for information systems.
Subject areas include data management issues as presented in the principal international database conferences (e.g., ACM SIGMOD/PODS, VLDB, ICDE and ICDT/EDBT) as well as data-related issues from the fields of data mining/machine learning, information retrieval coordinated with structured data, internet and cloud data management, business process management, web semantics, visual and audio information systems, scientific computing, and data science. Implementation papers having to do with massively parallel data management, fault tolerance in practice, and special purpose hardware for data-intensive systems are also welcome. Manuscripts from application domains, such as urban informatics, social and natural science, and Internet of Things, are also welcome. All papers should highlight innovative solutions to data management problems such as new data models, performance enhancements, and show how those innovations contribute to the goals of the application.