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Found 14 papers, 1 papers with code
Strahler Number of Natural Language Sentences in Comparison with Random Trees
The Strahler number was originally proposed to characterize the complexity of river bifurcation and has found various applications.
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.
Stock Embeddings Acquired from News Articles and Price History, and an Application to Portfolio Optimization
The stock embedding is acquired with a deep learning framework using both news articles and price history.
Extraction of Templates from Phrases Using Sequence Binary Decision Diagrams
The extraction of templates such as ``regard X as Y'' from a set of related phrases requires the identification of their internal structures.
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.
Word Familiarity and Frequency
Word frequency is assumed to correlate with word familiarity, but the strength of this correlation has not been thoroughly investigated.
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.
Taylor's law for Human Linguistic Sequences
Taylor's law describes the fluctuation characteristics underlying a system in which the variance of an event within a time span grows by a power law with respect to the mean.
Long-Range Correlation Underlying Childhood Language and Generative Models
Long-range correlation, a property of time series exhibiting long-term memory, is mainly studied in the statistical physics domain and has been reported to exist in 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.
Upper Bound of Entropy Rate Revisited ---A New Extrapolation of Compressed Large-Scale Corpora---
The article presents results of entropy rate estimation for human languages across six languages by using large, state-of-the-art corpora of up to 7. 8 gigabytes.
