Evidence map›Paper›PMID 35885082›Full record

ArticleEntropy (Basel, Switzerland)2022

Modeling Long-Range Dynamic Correlations of Words in Written Texts with Hawkes Processes.

Hiroshi Ogura, Yasutaka Hanada, Hiromi Amano, Masato Kondo

Abstract read
In one paragraph

Article in Entropy (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

4 authors.

Hiroshi OguraDepartment of Information Science, Faculty of Arts and Sciences, Showa University, Fujiyoshida 403-0005, Japan.
Yasutaka HanadaDepartment of Information Science, Faculty of Arts and Sciences, Showa University, Fujiyoshida 403-0005, Japan.
Hiromi AmanoDepartment of Information Science, Faculty of Arts and Sciences, Showa University, Fujiyoshida 403-0005, Japan.
Masato KondoDepartment of Information Science, Faculty of Arts and Sciences, Showa University, Fujiyoshida 403-0005, Japan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

It has been clarified that words in written texts are classified into two groups called Type-I and Type-II words. The Type-I words are words that exhibit long-range dynamic correlations in written texts while the Type-II words do not show any type of dynamic correlations. Although the stochastic process of yielding Type-II words has been clarified to be a superposition of Poisson point processes with various intensities, there is no definitive model for Type-I words. In this study, we introduce a Hawkes process, which is known as a kind of self-exciting point process, as a candidate for the stochastic process that governs yielding Type-I words; i.e., the purpose of this study is to establish that the Hawkes process is useful to model occurrence patterns of Type-I words in real written texts. The relation between the Hawkes process and an existing model for Type-I words, in which hierarchical structures of written texts are considered to play a central role in yielding dynamic correlations, will also be discussed.

Indexed as

autocorrelation functionHawkes processlong-range correlationstochastic processwaiting time distributionword occurrence

Identifiers

PMID35885082
PMCPMC9316552

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.