Urchade Zaratiana, Niama Elkhbir, Pierre Holat, Nadi Tomeh, Thierry Charnois
{"title":"Global Span Selection for Named Entity Recognition","authors":"Urchade Zaratiana, Niama Elkhbir, Pierre Holat, Nadi Tomeh, Thierry Charnois","doi":"10.18653/v1/2022.umios-1.2","DOIUrl":null,"url":null,"abstract":"Named Entity Recognition (NER) is an important task in Natural Language Processing with applications in many domains. In this paper, we describe a novel approach to named entity recognition, in which we output a set of spans (i.e., segmentations) by maximizing a global score. During training, we optimize our model by maximizing the probability of the gold segmentation. During inference, we use dynamic programming to select the best segmentation under a linear time complexity. We prove that our approach outperforms CRF and semi-CRF models for Named Entity Recognition. We will make our code publicly available.","PeriodicalId":360854,"journal":{"name":"Proceedings of the Workshop on Unimodal and Multimodal Induction of Linguistic Structures (UM-IoS)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the Workshop on Unimodal and Multimodal Induction of Linguistic Structures (UM-IoS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.18653/v1/2022.umios-1.2","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
Abstract
Named Entity Recognition (NER) is an important task in Natural Language Processing with applications in many domains. In this paper, we describe a novel approach to named entity recognition, in which we output a set of spans (i.e., segmentations) by maximizing a global score. During training, we optimize our model by maximizing the probability of the gold segmentation. During inference, we use dynamic programming to select the best segmentation under a linear time complexity. We prove that our approach outperforms CRF and semi-CRF models for Named Entity Recognition. We will make our code publicly available.
京公网安备 11010802042870号
京ICP备2023020795号-1
分享
求助内容：
应助结果提醒方式：
扫码关注我们
