Results 251 to 260 of about 637,325 (290)
Some of the next articles are maybe not open access.
Proceedings of the 23rd international conference on Machine learning - ICML '06, 2006
Some models of textual corpora employ text generation methods involving n-gram statistics, while others use latent topic variables inferred using the "bag-of-words" assumption, in which word order is ignored. Previously, these methods have not been combined.
openaire +1 more source
Some models of textual corpora employ text generation methods involving n-gram statistics, while others use latent topic variables inferred using the "bag-of-words" assumption, in which word order is ignored. Previously, these methods have not been combined.
openaire +1 more source
Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval, 2002
In this paper, we present a method based on document probes to quantify and diagnose topic structure, distinguishing topics as monolithic, structured, or diffuse. The method also yields a structure analysis that can be used directly to optimize filter (classifier) creation.
David A. Evans +2 more
openaire +1 more source
In this paper, we present a method based on document probes to quantify and diagnose topic structure, distinguishing topics as monolithic, structured, or diffuse. The method also yields a structure analysis that can be used directly to optimize filter (classifier) creation.
David A. Evans +2 more
openaire +1 more source
Proceedings of the 8th ACM Conference on Web Science, 2016
In this tutorial, we teach the intuition and the assumptions behind topic models. Topic models explain the co-occurrences of words in documents by extracting sets of semantically related words, called topics. These topics are semantically coherent and can be interpreted by humans. Starting with the most popular topic model, Latent Dirichlet Allocation (
Christoph Carl Kling +3 more
openaire +1 more source
In this tutorial, we teach the intuition and the assumptions behind topic models. Topic models explain the co-occurrences of words in documents by extracting sets of semantically related words, called topics. These topics are semantically coherent and can be interpreted by humans. Starting with the most popular topic model, Latent Dirichlet Allocation (
Christoph Carl Kling +3 more
openaire +1 more source
A bibliometric analysis of topic modelling studies (2000–2017)
Journal of Information Science, 2021Xin Li, Lei Lei
exaly
International Journal of Quality and Reliability Management, 2022
Federico Barravecchia +2 more
exaly
Federico Barravecchia +2 more
exaly
Topic modelling for theme park online reviews: analysis of Disneyland
Journal of Travel and Tourism Marketing, 2020Jian Ming Luo, Huy Quan Vu, Gang Li
exaly