Can Topic Modelling benefit from Word Sense Information?

被引：0
|
作者：
Ferrugento, Adriana [1 ]
Oliveira, Hugo Goncalo [1 ]
Alves, Ana Oliveira [1 ,2 ]
Rodrigues, Filipe [1 ]
机构：
[1] Univ Coimbra, Dept Informat Engn, CISUC, Coimbra, Portugal
[2] Polytech Inst Coimbra, IPC, Coimbra, Portugal
来源：
LREC 2016 - TENTH INTERNATIONAL CONFERENCE ON LANGUAGE RESOURCES AND EVALUATION | 2016年
关键词：
topic model; word senses; WordNet; semantics; SemLDA;
D O I：
暂无
中图分类号：
H [语言、文字];
学科分类号：
05 ;
摘要：
This paper proposes a new topic model that exploits word sense information in order to discover less redundant and more informative topics. Word sense information is obtained from WordNet and the discovered topics are groups of synsets, instead of mere surface words. A key feature is that all the known senses of a word are considered, with their probabilities. Alternative configurations of the model are described and compared to each other and to LDA, the most popular topic model. However, the obtained results suggest that there are no benefits of enriching LDA with word sense information.
引用
收藏
页码：3387 / 3393
页数：7
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