Zobrazeno 1 - 5
of 5
pro vyhledávání: '"Paulo Viana Bicalho"'
Publikováno v:
Information Sciences. 393:66-81
A framework to generate pseudo-documents suitable for topic modeling is proposed.An instantiation of the framework based on word vector representations is presented.Results of NPMI and F1 obtained are better than those of state-of-the art methods. Sh
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::c9e374c7415618d7878daa5992fff5aa
https://doi.org/10.1016/j.ins.2017.02.007
https://doi.org/10.1016/j.ins.2017.02.007
Autor:
Paulo Viana Bicalho
Publikováno v:
Repositório Institucional da UFMG
Universidade Federal de Minas Gerais (UFMG)
instacron:UFMG
Universidade Federal de Minas Gerais (UFMG)
instacron:UFMG
Short texts are everywhere in the Web, including messages posted in social media, status messages and blog comments, and uncovering the topics of this type of messages is crucial to a wide range of applications, e.g. context analysis and user charact
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od______3056::a7345ba99f941839f57c8b96f36e63da
Publikováno v:
BRACIS
Short texts are everywhere on the Web, including messages in social media, status messages, etc, and extracting semantically meaningful topics from these collections is an important and difficult task. Topic modeling methods, such as Latent Dirichlet
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::0c72ef8b234de2fe156a2d8288a612d7
https://doi.org/10.1109/bracis.2016.058
https://doi.org/10.1109/bracis.2016.058
Publikováno v:
BRACIS
Extracting topics from posts in social networks is a challenging and relevant computational task. Traditionally, topics are extracted by analyzing syntactic properties in the messages, assuming a high correlation between syntax and semantics. This wo
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::b9fdc106d041a2fa9bef46fc8f01a928
https://doi.org/10.1109/bracis.2014.56
https://doi.org/10.1109/bracis.2014.56
Autor:
Filipe de Lima Arcanjo, Paulo Viana Bicalho, Gisele L. Pappa, Altigran Soares da Silva, Wagner Meira
Publikováno v:
GECCO
Learning from unlabeled data provides innumerable advantages to a wide range of applications where there is a huge amount of unlabeled data freely available. Semi-supervised learning, which builds models from a small set of labeled examples and a pot
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::d4a1ad68880fecad3fa2605a9a383b0f
https://doi.org/10.1145/2001576.2001746
https://doi.org/10.1145/2001576.2001746