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Found 10 papers, 4 papers with code
Multilayer Networks for Text Analysis with Multiple Data Types
We are interested in the widespread problem of clustering documents and finding topics in large collections of written documents in the presence of metadata and hyperlinks.
A new evaluation framework for topic modeling algorithms based on synthetic corpora
Topic models are in widespread use in natural language processing and beyond.
A standardized Project Gutenberg corpus for statistical analysis of natural language and quantitative linguistics
The use of Project Gutenberg (PG) as a text corpus has been extremely popular in statistical analysis of language for more than 25 years.
A network approach to topic models
By adapting existing community-detection methods -- using a stochastic block model (SBM) with non-parametric priors -- we obtain a more versatile and principled framework for topic modeling (e. g., it automatically detects the number of topics and hierarchically clusters both the words and documents).
Generalized Entropies and the Similarity of Texts
This finding allows us to identify the contribution of specific words (and word frequencies) for the different generalized entropies and also to estimate the size of the databases needed to obtain a reliable estimation of the divergences.
Similarity of symbol frequency distributions with heavy tails
Quantifying the similarity between symbolic sequences is a traditional problem in Information Theory which requires comparing the frequencies of symbols in different sequences.
Statistical laws in linguistics
Zipf's law is just one out of many universal laws proposed to describe statistical regularities in language.
Scaling laws and fluctuations in the statistics of word frequencies
In this paper we combine statistical analysis of large text databases and simple stochastic models to explain the appearance of scaling laws in the statistics of word frequencies.
Extracting information from S-curves of language change
Combining data analysis with simulations of simple models (e. g., the Bass dynamics on complex networks) we identify signatures of endogenous and exogenous factors in the S-curves of adoption.
Stochastic model for the vocabulary growth in natural languages
We propose a stochastic model for the number of different words in a given database which incorporates the dependence on the database size and historical changes.
