AdaSeq基础能力 | 30+NER数据汇总,涉及多行业、多模态命名实体识别数据集收集
作者:落叶(达摩院的和尚,招实习生,求联系,见下文)
简介
命名实体识别NER是NLP基础任务,一直以来受到学术界和业界的广泛关注,本文汇总了常见的中英文、多语言、多模态NER数据集介绍。相关数据详情可以访问链接:https://github.com/modelscope/AdaSeq/blob/master/docs/datasets.md
进NLP群—>加入NLP交流群
一、中文数据集
首先我们先介绍常用的中文NER数据集,语料来源包括新闻、电商、文娱、医疗、微博、论文文献等。
MSRA命名实体识别数据集
简介:本数据集包括训练集(46364)、测试集(4365),实体类型包括地名(LOC)、人名(NAME)、组织名(ORG),数据源自新闻领域。 语种:Chinese "训练集/验证集/测试集"数量: 46364/-/4365 实体类别数量:3 论文:https://aclanthology.org/W06-0115.pdf 下载地址:https://tianchi.aliyun.com/dataset/144307
简历命名实体识别数据集
简介:本数据集包括训练集(3821)、验证集(463)、测试集(477),实体类型包括国籍(CONT)、教育背景(EDU)、地名(LOC)、人名(NAME)、组织名(ORG)、专业(PRO)、民族(RACE)、职称(TITLE),文本比较规范,实体识别模型效果通常F1 90%以上。 语种:Chinese "训练集/验证集/测试集"数量:3821/463/477 实体类别数量:9 论文:https://aclanthology.org/P18-1144.pdf 下载地址:https://tianchi.aliyun.com/dataset/144345 Github: https://github.com/jiesutd/LatticeLSTM
weibo命名实体识别数据集
简介:本数据集包括训练集(1350)、验证集(269)、测试集(270),实体类型包括地缘政治实体(GPE.NAM)、地名(LOC.NAM)、机构名(ORG.NAM)、人名(PER.NAM)及其对应的代指(以NOM为结尾),数据来自社交媒体平台,表达方式比较灵活。 语种:Chinese "训练集/验证集/测试集"数量: 1350/269/270 实体类别数量:4 论文:https://aclanthology.org/D15-1064.pdf 下载地址:https://tianchi.aliyun.com/dataset/144312 Github: https://github.com/hltcoe/golden-horse
OntoNotes Release 4.0
简介:OntoNotes Release 4.0 consists of 2.4 million words as follows: 300k words of Arabic newswire 250k words of Chinese newswire, 250k words of Chinese broadcast news, 150k words of Chinese broadcast conversation and 150k words of Chinese web text and 600k words of English newswire, 200k word of English broadcast news, 200k words of English broadcast conversation and 300k words of English web text. 语种:English, Mandarin Chinese, Arabic, Chinese "训练集/验证集/测试集"数量: 15724/4301/4346 下载地址:https://catalog.ldc.upenn.edu/LDC2011T03
OntoNotes Release 5.0
简介:OntoNotes Release 5.0 is the final release of the OntoNotes project, a collaborative effort between BBN Technologies, the University of Colorado, the University of Pennsylvania and the University of Southern Californias Information Sciences Institute. The goal of the project was to annotate a large corpus comprising various genres of text (news, conversational telephone speech, weblogs, usenet newsgroups, broadcast, talk shows) in three languages (English, Chinese, and Arabic) with structural information (syntax and predicate argument structure) and shallow semantics (word sense linked to an ontology and coreference). 语种:English "训练集/验证集/测试集"数量: 59924/8528/8262 论文:https://aclanthology.org/W13-3516.pdf 下载地址:https://catalog.ldc.upenn.edu/LDC2013T19
CLUENER2020 中文细粒度命名实体识别
简介:本数据是在清华大学开源的文本分类数据集THUCTC基础上,选出部分数据进行细粒度命名实体标注,原数据来源于Sina News RSS. 语种:Chinese "训练集/验证集/测试集"数量:10748/1343/1345 实体类别数量:10 论文:https://arxiv.org/ftp/arxiv/papers/2001/2001.04351.pdf 下载地址:https://tianchi.aliyun.com/dataset/144362 GitHub:https://github.com/CLUEbenchmark/CLUENER2020
人民日报NER数据集
简介:本NER数据集由人民日报语料库1998版和2014版生成,包含了人名(PER)、地名(LOC)和机构名(ORG)3类常见的实体类型。 语种:Chinese 实体类别数量:3 下载地址:https://github.com/InsaneLife/ChineseNLPCorpus/tree/master/NER/renMinRiBao
中文医学命名实体识别数据集CMeEE
简介:中文医学命名实体识别CMeEE,全称为Chinese Medical Entity Extraction dataset,来自于知名的中文医学NLP评测基准CBLUE。数据集包含504种常见的儿科疾病、7,085种身体部位、12,907种临床表现、4,354种医疗程序等九大类医学实体,包含训练集15,000条,验证集5,000条和测试集数据3,000条。CMeEE包括两个版本:CMeEE和CMeEE-V2(在CMeEE基础上更新了部分标注错误)。请研究人员到CBLUE项目主页下载:https://tianchi.aliyun.com/dataset/95414 语种:Chinese "训练集/验证集/测试集"数量: 15000/5000/3000 实体类别数量: 9 论文:https://aclanthology.org/2022.acl-long.544/ 下载地址:https://tianchi.aliyun.com/dataset/144495 Github: https://github.com/CBLUEbenchmark/CBLUE
Yidu-S4K:医渡云结构化4K数据集
简介:Yidu-S4K 数据集源自CCKS 2019 评测任务一,即“面向中文电子病历的命名实体识别”的数据集。 语种:Chinese "训练集/验证集/测试集"数量: 1000/-/379 实体类别数量:6 下载地址:https://tianchi.aliyun.com/dataset/144419
Youku NER Dataset / 文娱NER数据集
简介:命名体识别(NER)是一项重要的自然语言处理任务,本数据集提供了文娱领域的NER开放数据集,包括了3大类、9小类实体类别。该数据集由阿里巴巴达摩院和新加坡科技设计大学联合提供。 语种:Chinese "训练集/验证集/测试集"数量: 8,001/1,000/1,001 实体类别数量: 9 论文:https://aclanthology.org/N19-1079.pdf 下载地址:https://tianchi.aliyun.com/dataset/108771 Github: https://github.com/allanj/ner_incomplete_annotation
E-Commercial NER Dataset / 电商NER数据集
简介:命名体识别(NER)是一项重要的自然语言处理任务,本数据集提供了电商领域的NER开放数据集,包括了4大类、9小类实体类别。该数据集由阿里巴巴达摩院和新加坡科技设计大学联合提供。 语种:Chinese "训练集/验证集/测试集"数量: 6,000/998/1,000 实体类别数量: 9 论文:https://aclanthology.org/N19-1079.pdf 下载地址:https://tianchi.aliyun.com/dataset/108758 Github: https://github.com/allanj/ner_incomplete_annotation
Chinese-Literature-NER-RE-Dataset
简介:A Discourse-Level Named Entity Recognition and Relation Extraction Dataset for Chinese Literature Text. 语种:Chinese 实体类别数量:7 论文:https://arxiv.org/pdf/1711.07010.pdf 下载地址:https://tianchi.aliyun.com/dataset/144431 GitHub:https://github.com/lancopku/Chinese-Literature-NER-RE-Dataset
二、英文+多语言数据集
接下来我们介绍常用的英文和其它语种NER数据集,包括多模态NER的数据:
conll2002命名实体识别数据集
简介:CoNLL 2002和CoNLL 2003应该是NER开发者和研究人员常用的数据集了,分别是包含英语、俄语、西语、法语四种语言。每种语言的数据集涉及人名、地名、组织名和misc四类实体。 语种:Spanish, Dutch 实体类别数量:4 论文:https://aclanthology.org/W02-2024.pdf 下载地址:https://www.cnts.ua.ac.be/conll2002/ner/
conll2003命名实体识别数据集
简介:同上。 语种:English、German 实体类别数量:4 论文:https://aclanthology.org/W03-0419.pdf 下载地址:https://www.clips.uantwerpen.be/conll2003/ner/
wnut16命名实体识别数据集
简介:本数据集包括训练集(2394)、验证集(1000)、测试集(3850),实体类型包括company、facility、loc、movie、musicartist、other、person、product、sportsteam、tvshow。 语种:English "训练集/验证集/测试集"数量:2394/1000/3850 实体类别数量: 10 论文:https://aclanthology.org/W16-3919.pdf 下载地址:https://tianchi.aliyun.com/dataset/144348
wnut17命名实体识别数据集
简介:本数据集包括训练集(3394)、验证集(1009)、测试集(1287),实体类型包括corporation、creative-work、group、location、person、product。 语种:English "训练集/验证集/测试集"数量:3394/1009/1287 实体类别数量:6 论文:https://aclanthology.org/W17-4418.pdf 下载地址:https://tianchi.aliyun.com/dataset/144349
conllpp命名实体识别数据集
简介:本数据集包括训练集(14041)、验证集(3250)、测试集(3453),实体类型包括地点(LOC)、混合(MISC)、组织(ORG)、人名(PER)。conllpp数据集是conll数据集的修复版本。 语种:English "训练集/验证集/测试集"数量: 14041/3250/3453 实体类别数量:4 论文:https://aclanthology.org/D19-1519.pdf 下载地址:https://tianchi.aliyun.com/dataset/144414 Github: https://github.com/ZihanWangKi/CrossWeigh
CrossNER命名实体识别数据集
简介:CrossNER数据集是面向多个不同领域(文学、政治、音乐、科学、人工智能)的英文命名实体识别数据集,主要作为低资源NER的练兵场。 语种:English 论文:https://ojs.aaai.org/index.php/AAAI/article/view/17587/17394 下载地址:https://tianchi.aliyun.com/dataset/144418 Github: https://github.com/zliucr/CrossNER
BioCreative V CDR task corpus
简介:The BioCreative V CDR task corpus is manually annotated for chemicals, diseases and chemical-induced disease (CID) relations. It contains the titles and abstracts of 1500 PubMed articles and is split into equally sized train, validation and test sets. 语种:English "训练集/验证集/测试集"数量:4560/4581/4797 实体类别数量:2 论文:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4860626/ 下载地址:https://biocreative.bioinformatics.udel.edu/tasks/biocreative-v/track-3-cdr/
NCBI disease corpus
简介:The NCBI disease corpus is fully annotated at the mention and concept level to serve as a research resource for the biomedical natural language processing community. 语种:English "训练集/验证集/测试集"数量:5424/923/940 实体类别数量:1 论文:https://pubmed.ncbi.nlm.nih.gov/24393765/ 下载地址:https://www.ncbi.nlm.nih.gov/CBBresearch/Dogan/DISEASE/
MIT-Movie命名实体识别数据集
简介:The MIT Movie Corpus is a semantically tagged training and test corpus in BIO format in the movie domain. 语种:English, Chinese "训练集/验证集/测试集"数量:6816/1000/1953 实体类别数量: 12 论文:https://groups.csail.mit.edu/sls/publications/2013/Liu_ICASSP-2013.pdf 下载地址:https://tianchi.aliyun.com/dataset/145106
MIT-Restaurant命名实体识别数据集
简介:MIT Restaurant Corpus 是餐厅领域中 BIO 格式的实体识别语料库。 语种:English, Chinese "训练集/验证集/测试集"数量:6900/760/1521 实体类别数量: 9 论文:https://groups.csail.mit.edu/sls/publications/2013/Liu_ICASSP-2013.pdf 下载地址:https://tianchi.aliyun.com/dataset/145105
ACE 2004 Multilingual Training Corpus
简介:This corpus represents the complete set of English, Arabic, and Chinese training data for the 2004 Automatic Content Extraction (ACE) technology evaluation created by LDC with support from the ACE Program and additional assistance from the DARPA TIDES (Translingual Information Detection, Extraction and Summarization) Program. This data was previously distributed as an e-corpus (LDC2004E17) to participants in the 2004 ACE evaluation. 语种:English,Arabic, and Chinese 论文:http://www.lrec-conf.org/proceedings/lrec2004/pdf/5.pdf 下载地址:https://catalog.ldc.upenn.edu/LDC2005T09
ACE 2005 Multilingual Training Corpus
简介:ACE 2005 Multilingual Training Corpus was developed by the Linguistic Data Consortium (LDC) and contains approximately 1,800 files of mixed genre text in English, Arabic, and Chinese annotated for entities, relations, and events. This represents the complete set of training data in those languages for the 2005 Automatic Content Extraction (ACE) technology evaluation. The genres include newswire, broadcast news, broadcast conversation, weblog, discussion forums, and conversational telephone speech. The data was annotated by LDC with support from the ACE Program and additional assistance from LDC. 语种:English,Arabic, and Chinese 下载地址:https://catalog.ldc.upenn.edu/LDC2006T06
KBP2017命名实体识别数据集
简介:The Entity Discovery and Linking (EDL) track aims to extract entity mentions from a source collection of textual documents in multiple languages, and link them to a reference knowledge base; an EDL system is also required to cluster mentions for those entities that don't have corresponding KB entries. 语种:English 实体类别数量: 5 论文:https://tac.nist.gov/publications/2017/additional.papers/TAC2017.KBP_Entity_Discovery_and_Linking_overview.proceedings.pdf 下载地址:https://catalog.ldc.upenn.edu/LDC2019T19 任务官网:https://tac.nist.gov/2017/KBP/
JNLPBA生物命名体识别数据集
简介:The BioNLP / JNLPBA Shared Task 2004 involves the identification and classification of technical terms referring to concepts of interest to biologists in the domain of molecular biology. The task was organized by GENIA Project based on the annotations of the GENIA Term corpus (version 3.02). 语种:English "训练集/验证集/测试集"数量: 2000/-/404 实体类别数量: 5 论文:https://dl.acm.org/doi/10.5555/1567594.1567610 下载地址:https://tianchi.aliyun.com/dataset/144943
Few-NERD
简介:Few-NERD是一个大规模,多粒度的人工标注命名实体识别(Named Entity Recognition, NER)数据集,包含了8个大类,66个小类,18万余个句子,49余万个实体。本数据集包括3个任务,分别为标准监督NER(Few-NERD (SUP)),跨大类Few-shot NER(Few-NERD (INTRA))和不跨大类的Few-shot NER (Few-NERD (INTER))。Few-NERD由清华大学和阿里巴巴的研究者构建而成。 语种:English "训练集/验证集/测试集"数量:131767/18824/37548 实体类别数量: 8 / 66 论文:https://aclanthology.org/2021.acl-long.248.pdf 下载地址:https://tianchi.aliyun.com/dataset/102048 Github: https://github.com/thunlp/Few-NERD
Financial NER Dataset
简介:The dataset is generated using CoNll2003 data and financial documents obtained from U.S. Security and Exchange Commission (SEC) filings. 语种:English "训练集/验证集/测试集"数量: (Document level) 5/-/3 实体类别数量: 4 论文:https://aclanthology.org/U15-1010/ 下载地址:https://tianchi.aliyun.com/dataset/145092
Broad Twitter Corpus (BTC)
简介:The Broad Twitter Corpus is a named entity-annotated dataset of tweets, collected in order to capture temporal, spatial and social diversity. Its annotations have high agreement and quality, and it has about 12000 entity annotations, of types Person, Location and Organization. 语种:English "训练集/验证集/测试集"数量:6338/1001/2000 实体类别数量:3 论文:https://aclanthology.org/C16-1111.pdf 下载地址:https://tianchi.aliyun.com/dataset/145001 Github: https://github.com/GateNLP/broad_twitter_corpus
Temporal Twitter Corpus (TTC)
简介:It includes 12,000 tweets annotated for the named entity recognition task. The tweets are uniformly distributed over the years 2014-2019, with 2,000 tweets from each year. The goal is to have a temporally diverse corpus to account for data drift over time when building NER models. 语种:English "训练集/验证集/测试集"数量: 10000/500/1500 实体类别数量: 3 论文:https://aclanthology.org/2020.acl-main.680.pdf 下载地址:https://tianchi.aliyun.com/dataset/144438 GitHub:https://github.com/shrutirij/temporal-twitter-corpus
Tweebank-NER
简介:Social media data such as Twitter messages (“tweets”) pose a particular challenge to NLP systems because of their short, noisy, and colloquial nature. The Tweebank-NER is an English NER corpus based on Tweebank V2 (TB2). 语种:English "训练集/验证集/测试集"数量: 1,639/710/1,201 实体类别数量:4 论文:https://aclanthology.org/2022.lrec-1.780.pdf 下载地址:https://tianchi.aliyun.com/dataset/145049 Github:https://github.com/mit-ccc/TweebankNLP
TweetNER7
简介:TweetNER7 is a NER dataset on Twitter with 7 entity labels annotated over 11,382 tweets from September 2019 to August 2021. 语种:English 实体类别数量: 7 论文:https://aclanthology.org/2022.aacl-main.25.pdf 下载地址:https://tianchi.aliyun.com/dataset/145052 HuggingFace:https://huggingface.co/datasets/tner/tweetner7/tree/main/dataset
三、多模态NER数据集
接下来我们介绍常用多模态NER的数据:
Multimodal Twitter-15 NER Dataset
简介:来自社交媒体领域的多模态NER数据集,内容来自推文及其图片。 语种:English "训练集/验证集/测试集"数量: 4000/1000/3257 实体类别数量:4 论文:https://ojs.aaai.org/index.php/AAAI/article/view/11962/11821 下载地址:https://tianchi.aliyun.com/dataset/145058 GitHub:https://github.com/jinlanfu/NERmultimodal
Multimodal Twitter-17 NER Dataset
简介:与上面类似,来自社交媒体领域的多模态NER数据集,内容来自推文及其图片。多模态NER的论文通常会在这两个数据集上进行实验。 语种:English "训练集/验证集/测试集"数量: 4000/1000/3257 实体类别数量:4 论文:https://aclanthology.org/2020.acl-main.306.pdf 下载地址:https://github.com/jefferyYu/UMT GitHub:https://github.com/jefferyYu/UMT
Multimodal SNAP NER Dataset
简介:SNAP的多模态NER数据,实体类型分别是人名、地名、组织名和misc。 语种:English 实体类别数量:4 论文:https://aclanthology.org/P18-1185.pdf 下载地址:https://github.com/jefferyYu/UMT GitHub:https://github.com/jefferyYu/UMT
WikiDiverse Dataset
简介:是一个多模态实体识别和实体链接数据集。这一数据集是基于多个角度的考虑:首先,综合参考现有的实体链接数据集、分析图文匹配程度、实体消歧难度等信息,采用WikiNews的“图片-标题”对作为原始数据,将Wikipedia作为对应的知识图谱。其次,我们采集了体育、政治、娱乐、灾难、科技、犯罪、经济、教育、健康、天气主题的图文对,并进行了质量低下、色情、暴恐信息的清洗,对图片类型进行了归一化(因为部分图片为gif等格式),从而保证数据的高覆盖性和质量。最后,引入了众包标注平台进行数据标注,在此过程中设计了详细的标注规范,特别地,我们关注人物、组织、地点、国家、事件、作品(包含图书、画作等)、其他等多个实体类型。 语种:English "训练集/验证集/测试集"数量: 6312/755/757 论文:https://aclanthology.org/2022.acl-long.328.pdf 下载地址:https://tianchi.aliyun.com/dataset/145103 GitHub:https://github.com/wangxw5/wikidiverse
四、 多语言NER数据集
接下来我们介绍常用的多语种NER的数据:
MultiCoNER Dataset
简介:MultiCoNER 是用于命名实体识别的大型多语言数据集(11 种语言)。它旨在代表 NER 中的一些当代挑战,包括低上下文场景(短文本和无大小写文本)、句法复杂的实体(如电影片名)和长尾实体分布。 语种:Bangla、 Chinese、Dutch、English、Farsi、German、Hindi、Korean、Russian、Spanish、Turkish. 实体类别数量:6 论文:https://aclanthology.org/2022.coling-1.334/ 下载地址:https://tianchi.aliyun.com/dataset/145100 任务官网:https://multiconer.github.io/multiconer_1/
命名实体识别数据集汇总列表
Language | Dataset | Size | #Types | Description | Paper | Download |
---|---|---|---|---|---|---|
Chinese | msra | 46364/-/4365 | 3 | Levow | damo/msra_ner | |
Chinese | resume | 3821/463/477 | 9 | Zhang & Yang | damo/resume_ner | |
Chinese | 1350/269/270 | 4 | Peng & Dredze | damo/weibo_ner | ||
Chinese | ontonotes-v4-zh | 15724/4301/4346 | - | ldc/ontonotes-v4 | ||
Chinese | cluener2020 | 10748/1343/1345 | 10 | Xu et al., 2020 | github/cluener2020 | |
Chinese | people_dairy1998 | 3 | github/ChineseNLPCorpus | |||
Chinese | people_dairy2014 | 3 | baidu-pan passwrod:1fa3 | |||
Chinese | cmeee | 15000/5000/3000 | CMeEE dataset in CBLUE benchmark | Zhang et al., 2022 | github/cblue | |
Chinese | yidu-s4k | - | openkg/yidu-s4k | |||
Chinese | ecommerce | Jie et al., 2019 | github/ner_incomplete_annotation/ecommerce | |||
Chinese | dlner | Xu, et al.,2017 | github/dlner | |||
Dutch | conll2002-nl | 15796/2895/5196 | 4 | Tjong Kim Sang, 2002 | ||
English | wnut2016 | 2394/1000/3850 | Noisy User-generated Text | Strauss et al., 2016 | damo/wnut16 | |
English | wnut2017 | 3394/1009/1287 | Derczynski et al., 2017 | damo/wnut17 | ||
English | conll2003-en | 14041/3250/3453 | 4 | Tjong Kim Sang & De Meulder, 2003 | ||
English | conllpp | 14041/3250/3453 | 4 | corrected version of the conll03-en NER dataset | Wang et al., 2019 | damo/conllpp_ner |
English | ontonotes-v5-en | 59924/8528/8262(TBD) | Pradhan et al., 2013 | ldc/ontonotes-v5 | ||
English | ai | 100/350/431 | Liu et al., 2020 | damo/cross_ner | ||
English | literature | 100/400/416 | Liu et al., 2020 | damo/cross_ner | ||
English | music | 100/541/465 | Liu et al., 2020 | damo/cross_ner | ||
English | politics | 200/541/651 | Liu et al., 2020 | damo/cross_ner | ||
English | science | 200/450/543 | Liu et al., 2020 | damo/cross_ner | ||
English | bc5cdr | 4560/4581/4797 | Li et al., 2016 | |||
English | ncbi | 5424/923/940 | Doğan et al., 2014 | |||
English | mit-movie | 6816/1000/1953(TBD) | Liu et al., 2013 | mit/movie | ||
English | mit-restaurant | 6900/760/1521 | Liu et al., 2013 | mit/restaurant | ||
English | ace2004-en | 7 | nested ner | Doddington et al., 2005 | ldc/ace04 | |
English | ace2005-en | 7 | nested ner | - | ldc/ace05 | |
English | kbp2017 | nested ner | - | - | ||
English | genia | nested ner | Ohta et al., 2002 | |||
English | few-nerd | 131767/18824/37548 | 8 / 66 | a few-shot ner dataset | Ding et al., 2021 | |
English | wikigold | Balasuriya et al.,2009 | ||||
English | bionlp2014 | Collier & Kim, 2004 | ||||
English | fin | Alvarado et al., 2015 | ||||
English | btc | 6338/1001/2000 | 3 | Derczynski et al., 2016 | ||
English | ttc | Rijhwani & Preot¸iuc-Pietro | github/ttc | |||
English | tweebank | Jiang et al.,2022 | github/tweebank | |||
English | tweetner7 | Ushio, et al., 2022 | huggingface/tweetner7 | |||
German | conll2003-de | 12152/2866/3005 | 4 | Tjong Kim Sang & De Meulder, 2003 | ||
Spanish | conll2002-es | 8302/1919/1517 | 4 | Tjong Kim Sang, 2002 | ||
English | twitter2015 | multi-modal | Zhang et al., 2018 | |||
English | snap | multi-modal | Lu et al., 2018 | github/UMT | ||
English | twitter2017 | multi-modal | Yu et al., 2020 | github/UMT | ||
English | wiki-diverse | constructed from wiki-diverse (a multi-modal entity typing dataset) | Wang et al., 2022 | github/wikidiverse | ||
11 langs | multiconer2022 | - | 6 | dataset of SemEval 2022 Task 11 | ||
(English, Spanish, Dutch, Russian, Turkish, Korean, Farsi, German, Chinese, Hindi, and Bangla) | Malmasi et al., 2022 | aws/multiconer | ||||
282 langs | wikiann | - | silver-standard data | Pan et al, 2017 | github/wikiann | |
9 langs | wikiner | - | silver-standard data | Nothman et al, 2013 | ||
9 langs | wikineural | - | silver-standard data | Tedeschi et al, 2021 | ||
10 langs | multinerd | - | silver-standard data | Tedeschi & Navigli. 2022 |
致谢
本列表由达摩院NLP团队和天池数据科学团队长期维护,相关数据可以通过序列理解统一框架AdaSeq进行模型训练。https://github.com/modelscope/AdaSeq/blob/master/README_zh.md
欢迎关注我的视频号~
微信扫码关注该文公众号作者
戳这里提交新闻线索和高质量文章给我们。
来源: qq
点击查看作者最近其他文章