Automatic Keyword Extraction on Twitter

被引:0
|
作者
Marujo, Luis [1 ,2 ,3 ]
Ling, Wang [1 ,2 ,3 ]
Trancoso, Isabel [2 ,3 ]
Dyer, Chris [1 ]
Black, Alan W. [1 ]
Gershman, Anatole [1 ]
de Matos, David Martins [2 ,3 ]
Neto, Joao P. [2 ,3 ]
Carbonell, Jaime [1 ]
机构
[1] Carnegie Mellon Univ, Language Technol Inst, Pittsburgh, PA 15213 USA
[2] Univ Lisbon, Inst Super Tecn, Lisbon, Portugal
[3] INESC ID, Lisbon, Portugal
基金
美国安德鲁·梅隆基金会;
关键词
SEARCH;
D O I
暂无
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
In this paper, we build a corpus of tweets from Twitter annotated with keywords using crowdsourcing methods. We identify key differences between this domain and the work performed on other domains, such as news, which makes existing approaches for automatic keyword extraction not generalize well on Twitter datasets. These datasets include the small amount of content in each tweet, the frequent usage of lexical variants and the high variance of the cardinality of keywords present in each tweet. We propose methods for addressing these issues, which leads to solid improvements on this dataset for this task.
引用
收藏
页码:637 / 643
页数:7
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