Mental Mechanisms for Topics Identification
Author
Source
Computational Intelligence and Neuroscience
Issue
Vol. 2014, Issue 2014 (31 Dec. 2014), pp.1-11, 11 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2014-03-13
Country of Publication
Egypt
No. of Pages
11
Main Subjects
Abstract EN
Topics identification (TI) is the process that consists in determining the main themes present in natural language documents.
The current TI modeling paradigm aims at acquiring semantic information from statistic properties of large text datasets.
We investigate the mental mechanisms responsible for the identification of topics in a single document given existing knowledge.
Our main hypothesis is that topics are the result of accumulated neural activation of loosely organized information stored in long-term memory (LTM).
We experimentally tested our hypothesis with a computational model that simulates LTM activation.
The model assumes activation decay as an unavoidable phenomenon originating from the bioelectric nature of neural systems.
Since decay should negatively affect the quality of topics, the model predicts the presence of short-term memory (STM) to keep the focus of attention on a few words, with the expected outcome of restoring quality to a baseline level.
Our experiments measured topics quality of over 300 documents with various decay rates and STM capacity.
Our results showed that accumulated activation of loosely organized information was an effective mental computational commodity to identify topics.
It was furthermore confirmed that rapid decay is detrimental to topics quality but that limited capacity STM restores quality to a baseline level, even exceeding it slightly.
American Psychological Association (APA)
Massey, Louis. 2014. Mental Mechanisms for Topics Identification. Computational Intelligence and Neuroscience،Vol. 2014, no. 2014, pp.1-11.
https://search.emarefa.net/detail/BIM-508270
Modern Language Association (MLA)
Massey, Louis. Mental Mechanisms for Topics Identification. Computational Intelligence and Neuroscience No. 2014 (2014), pp.1-11.
https://search.emarefa.net/detail/BIM-508270
American Medical Association (AMA)
Massey, Louis. Mental Mechanisms for Topics Identification. Computational Intelligence and Neuroscience. 2014. Vol. 2014, no. 2014, pp.1-11.
https://search.emarefa.net/detail/BIM-508270
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-508270