Twitter上关于新英格兰爱国者队泄气门（Deflategate）事件的情绪免费

Khan 29 2021-08-24 NLP
点击图片放大查看

资源介绍

新英格兰爱国者队在美国橄榄球联合会（AFC）决赛中击败印第安纳波利斯小马队，将在2月1号的超级碗大赛中迎战西雅图海鹰队。但是爱国者队被发现在这次比赛中使用的12个橄榄球有11个充气不足。这个数据集可用于观察丑闻爆发Twitter用户的情绪，以衡量公众对整个事件的看法

END
标签
上一篇
下一篇

发表评论

会员价:￥0推荐
原价:￥0
暂无演示 登录下载
登录

最新文章

更多>

热门文章

更多>

MIMIC-III（"重症监护医疗信息市场"）

2021-08-24 163

QASC: A Dataset for Question Answering via Sentence CompositionComposing knowledge from multiple pieces of texts is a key challenge in multi-hop question answering. We present a multi-hop reasoning dataset, Question Answering via Sentence Composition(QASC), that requires retrieving facts from a large corpus and composing them to answer a multiple-choice question. QASC is the first dataset to offer two desirable properties: (a) the facts to be composed are annotated in a large corpus, and (b) the decomposition into these facts is not evident from the question itself. The latter makes retrieval challenging as the system must introduce new concepts or relations in order to discover potential decompositions. Further, the reasoning model must then learn to identify valid compositions of these retrieved facts using common-sense reasoning. To help address these challenges, we provide annotation for supporting facts as well as their composition. Guided by these annotations, we present a two-step approach to mitigate the retrieval challenges. We use other multiple-choice datasets as additional training data to strengthen the reasoning model. Our proposed approach improves over current state-of-the-art language models by 11% (absolute). The reasoning and retrieval problems, however, remain unsolved as this model still lags by 20% behind human performance.

2021-08-24 146

Yahoo!从公开可用网页中提取元数据

2021-08-24 90

RyanSpeech: A Corpus for Conversational Text-to-Speech Synthesis

2021-08-24 75

The SOFC-Exp Corpus and Neural Approaches to Information Extraction in the Materials Science Domain

2021-08-24 75

标签云

猜你喜欢

MIMIC-III（"重症监护医疗信息市场"）
QASC: A Dataset for Question Answering via Sentence CompositionComposing knowledge from multiple pieces of texts is a key challenge in multi-hop question answering. We present a multi-hop reasoning dataset, Question Answering via Sentence Composition(QASC), that requires retrieving facts from a large corpus and composing them to answer a multiple-choice question. QASC is the first dataset to offer two desirable properties: (a) the facts to be composed are annotated in a large corpus, and (b) the decomposition into these facts is not evident from the question itself. The latter makes retrieval challenging as the system must introduce new concepts or relations in order to discover potential decompositions. Further, the reasoning model must then learn to identify valid compositions of these retrieved facts using common-sense reasoning. To help address these challenges, we provide annotation for supporting facts as well as their composition. Guided by these annotations, we present a two-step approach to mitigate the retrieval challenges. We use other multiple-choice datasets as additional training data to strengthen the reasoning model. Our proposed approach improves over current state-of-the-art language models by 11% (absolute). The reasoning and retrieval problems, however, remain unsolved as this model still lags by 20% behind human performance.
Yahoo!从公开可用网页中提取元数据
The SOFC-Exp Corpus and Neural Approaches to Information Extraction in the Materials Science Domain
RyanSpeech: A Corpus for Conversational Text-to-Speech Synthesis
Chinese handwritten digits MNIST dataset
博客作者身份语料库
通过安装残余物来在自然语言推理中未学习数据集偏差
济州岛数据集用于机器翻译和语音合成
Yahoo! Answers Comprehensive Questions and Answers