@inproceedings{ferrugento-etal-2016-topic,
title = "Can Topic Modelling benefit from Word Sense Information?",
author = "Ferrugento, Adriana and
Oliveira, Hugo Gon{\c{c}}alo and
Alves, Ana and
Rodrigues, Filipe",
editor = "Calzolari, Nicoletta and
Choukri, Khalid and
Declerck, Thierry and
Goggi, Sara and
Grobelnik, Marko and
Maegaard, Bente and
Mariani, Joseph and
Mazo, Helene and
Moreno, Asuncion and
Odijk, Jan and
Piperidis, Stelios",
booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
month = may,
year = "2016",
address = "Portoro{\v{z}}, Slovenia",
publisher = "European Language Resources Association (ELRA)",
url = "https://aclanthology.org/L16-1540",
pages = "3387--3393",
abstract = "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.",
}
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%0 Conference Proceedings
%T Can Topic Modelling benefit from Word Sense Information?
%A Ferrugento, Adriana
%A Oliveira, Hugo Gonçalo
%A Alves, Ana
%A Rodrigues, Filipe
%Y Calzolari, Nicoletta
%Y Choukri, Khalid
%Y Declerck, Thierry
%Y Goggi, Sara
%Y Grobelnik, Marko
%Y Maegaard, Bente
%Y Mariani, Joseph
%Y Mazo, Helene
%Y Moreno, Asuncion
%Y Odijk, Jan
%Y Piperidis, Stelios
%S Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC’16)
%D 2016
%8 May
%I European Language Resources Association (ELRA)
%C Portorož, Slovenia
%F ferrugento-etal-2016-topic
%X 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.
%U https://aclanthology.org/L16-1540
%P 3387-3393
Markdown (Informal)
[Can Topic Modelling benefit from Word Sense Information?](https://aclanthology.org/L16-1540) (Ferrugento et al., LREC 2016)
ACL
- Adriana Ferrugento, Hugo Gonçalo Oliveira, Ana Alves, and Filipe Rodrigues. 2016. Can Topic Modelling benefit from Word Sense Information?. In Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16), pages 3387–3393, Portorož, Slovenia. European Language Resources Association (ELRA).