Results 141 to 150 of about 3,238,976 (181)
Some of the next articles are maybe not open access.
Proceedings of the 23rd international conference on Machine learning - ICML '06, 2006
A family of probabilistic time series models is developed to analyze the time evolution of topics in large document collections. The approach is to use state space models on the natural parameters of the multinomial distributions that represent the topics.
Blei, David M., Lafferty, John D.
openaire +1 more source
A family of probabilistic time series models is developed to analyze the time evolution of topics in large document collections. The approach is to use state space models on the natural parameters of the multinomial distributions that represent the topics.
Blei, David M., Lafferty, John D.
openaire +1 more source
Neurocomputing, 2012
In this paper the problem of performing external validation of the semantic coherence of topic models is considered. The Fowlkes-Mallows index, a known clustering validation metric, is generalized for the case of overlapping partitions and multi-labeled collections, thus making it suitable for validating topic modeling algorithms.
Ramirez, E +3 more
openaire +2 more sources
In this paper the problem of performing external validation of the semantic coherence of topic models is considered. The Fowlkes-Mallows index, a known clustering validation metric, is generalized for the case of overlapping partitions and multi-labeled collections, thus making it suitable for validating topic modeling algorithms.
Ramirez, E +3 more
openaire +2 more sources
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