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Found 6 papers, 5 papers with code
Heaps' Law in GPT-Neo Large Language Model Emulated Corpora
Heaps' law is an empirical relation in text analysis that predicts vocabulary growth as a function of corpus size.
A statistical significance testing approach for measuring term burstiness with applications to domain-specific terminology extraction
However, the document frequency of a term (i. e., the proportion of documents within a corpus in which a specific term occurs) is exploited by certain other widely used term burstiness measures.
The hypergeometric test performs comparably to TF-IDF on standard text analysis tasks
Term frequency-inverse document frequency, or TF-IDF for short, and its many variants form a class of term weighting functions the members of which are widely used in text analysis applications.
The Literary Theme Ontology for Media Annotation and Information Retrieval
Literary theme identification and interpretation is a focal point of literary studies scholarship.
An Ontology-Based Recommender System with an Application to the Star Trek Television Franchise
As a study case, we evaluated the proposed method against other approaches by performing the classical rating prediction task on a collection of Star Trek television series episodes in an item cold-start scenario.
PAFit: an R Package for the Non-Parametric Estimation of Preferential Attachment and Node Fitness in Temporal Complex Networks
This paper introduces the R package PAFit, which implements non-parametric procedures for estimating the preferential attachment function and node fitnesses in a growing network, as well as a number of functions for generating complex networks from these two mechanisms.
Data Analysis, Statistics and Probability Social and Information Networks Physics and Society Computation
