Search Results for author:
Found 4 papers, 0 papers with code
A Comparison of Two Fluctuation Analyses for Natural Language Clustering Phenomena: Taylor and Ebeling & Neiman Methods
This article considers the fluctuation analysis methods of Taylor and Ebeling & Neiman.
Evaluating Computational Language Models with Scaling Properties of Natural Language
Statistical mechanical analyses have revealed that natural language text is characterized by scaling properties, which quantify the global structure in the vocabulary population and the long memory of a text.
Assessing Language Models with Scaling Properties
Five such tests are considered, with the first two accounting for the vocabulary population and the other three for the long memory of natural language.
Do Neural Nets Learn Statistical Laws behind Natural Language?
Precisely, we demonstrate that a neural language model based on long short-term memory (LSTM) effectively reproduces Zipf's law and Heaps' law, two representative statistical properties underlying natural language.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
