Analysis of named entity recognition and linking for tweets
about
Harnessing Diversity in Crowds and Machines for Better NER PerformanceMining e-cigarette adverse events in social media using Bi-LSTM recurrent neural network with word embedding representation.Social media analysis during political turbulence.Entity Grouping for Accessing Social Streams via Word CloudsMedia trustworthiness verification and event assessment through an integrated framework: a case-studyAn extended study of content and crowdsourcing-related performance factors in named entity annotationExploring open information via event networkBroad Twitter Corpus: A Diverse Named Entity Recognition Resource
P2860
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P2860
Analysis of named entity recognition and linking for tweets
description
2015 nî lūn-bûn
@nan
2015年の論文
@ja
2015年学术文章
@wuu
2015年学术文章
@zh
2015年学术文章
@zh-cn
2015年学术文章
@zh-hans
2015年学术文章
@zh-my
2015年学术文章
@zh-sg
2015年學術文章
@yue
2015年學術文章
@zh-hant
name
Analysis of named entity recognition and linking for tweets
@en
Analysis of named entity recognition and linking for tweets
@nl
type
label
Analysis of named entity recognition and linking for tweets
@en
Analysis of named entity recognition and linking for tweets
@nl
prefLabel
Analysis of named entity recognition and linking for tweets
@en
Analysis of named entity recognition and linking for tweets
@nl
P50
P1476
Analysis of named entity recognition and linking for tweets
@en
P2093
Johann Petrak
Marieke van Erp
P356
10.1016/J.IPM.2014.10.006
P577
2015-03-01T00:00:00Z