scholarly journals Strong correlations between text quality and complex networks features

2007 ◽  
Vol 373 ◽  
pp. 811-820 ◽  
Author(s):  
L. Antiqueira ◽  
M.G.V. Nunes ◽  
O.N. Oliveira Jr. ◽  
L. da F. Costa
Keyword(s):  
Complex Networks ◽  
Strong Correlations ◽  
Text Quality

COMPLEX NETWORKS ANALYSIS OF MANUAL AND MACHINE TRANSLATIONS

2008 ◽  
Vol 19 (04) ◽  
pp. 583-598 ◽  
Author(s):  
DIEGO R. AMANCIO ◽  
LUCAS ANTIQUEIRA ◽  
THIAGO A. S. PARDO ◽  
LUCIANO da F. COSTA ◽  
OSVALDO N. OLIVEIRA ◽  
...  
Keyword(s):  
Complex Networks ◽  
Machine Translation ◽  
Language Processing ◽  
Text Analysis ◽  
Shortest Paths ◽  
Quality Measurement ◽  
Clustering Coefficient ◽  
Text Quality ◽  
Text Features ◽  
Networks Analysis

Complex networks have been increasingly used in text analysis, including in connection with natural language processing tools, as important text features appear to be captured by the topology and dynamics of the networks. Following previous works that apply complex networks concepts to text quality measurement, summary evaluation, and author characterization, we now focus on machine translation (MT). In this paper we assess the possible representation of texts as complex networks to evaluate cross-linguistic issues inherent in manual and machine translation. We show that different quality translations generated by MT tools can be distinguished from their manual counterparts by means of metrics such as in- (ID) and out-degrees (OD), clustering coefficient (CC), and shortest paths (SP). For instance, we demonstrate that the average OD in networks of automatic translations consistently exceeds the values obtained for manual ones, and that the CC values of source texts are not preserved for manual translations, but are for good automatic translations. This probably reflects the text rearrangements humans perform during manual translation. We envisage that such findings could lead to better MT tools and automatic evaluation metrics.

Interrelationships of factors of social development are more complex than Life History Theory predicts

2019 ◽  
Vol 42 ◽  
Author(s):  
Boris Kotchoubey
Keyword(s):  
Life History ◽  
Social Development ◽  
Life History Theory ◽  
Strong Correlations ◽  
Behavioral Features

Abstract Life History Theory (LHT) predicts a monotonous relationship between affluence and the rate of innovations and strong correlations within a cluster of behavioral features. Although both predictions can be true in specific cases, they are incorrect in general. Therefore, the author's explanations may be right, but they do not prove LHT and cannot be generalized to other apparently similar processes.

Dielectronic Recombination in the Average-Atom Model

1988 ◽  
Vol 102 ◽  
pp. 215
Author(s):  
R.M. More ◽  
G.B. Zimmerman ◽  
Z. Zinamon
Keyword(s):  
Detailed Balance ◽  
Systematic Errors ◽  
Dielectronic Recombination ◽  
Rate Equations ◽  
Strong Correlations ◽  
Dense Plasmas ◽  
Atom Model ◽  
New Feature ◽  
Average Atom Model ◽  
Attachment Process

Autoionization and dielectronic attachment are usually omitted from rate equations for the non–LTE average–atom model, causing systematic errors in predicted ionization states and electronic populations for atoms in hot dense plasmas produced by laser irradiation of solid targets. We formulate a method by which dielectronic recombination can be included in average–atom calculations without conflict with the principle of detailed balance. The essential new feature in this extended average atom model is a treatment of strong correlations of electron populations induced by the dielectronic attachment process.

Complex Networks

2009 ◽  
Author(s):  
Reuven Cohen ◽  
Shlomo Havlin
Keyword(s):  
Complex Networks

Intrinsic Characteristics of Quasars Redshifts

2014 ◽  
Vol 4 (3) ◽  
pp. 655-661
Author(s):  
Waleed Elsanhoury
Keyword(s):  
Absolute Magnitude ◽  
Sloan Digital Sky Survey ◽  
Strong Correlations ◽  
Sky Survey

Using Sloan Digital Sky Survey SDSS catalog, some intrinsic characteristics of Quasars (10,000 points) are developed of these are the strong correlations between redshifts and other parameters, e.g. combined magnitude, luminosity, and absolute magnitude .Moreover ,the Karlsson peak of our sample is also computed.

Complex Networks from Simple Rules

2013 ◽  
Vol 22 (2) ◽  
pp. 151-174 ◽  
Author(s):  
Richard Southwell ◽  
Jianwei Huang ◽  
Chris Cannings ◽  
◽  
Keyword(s):  
Complex Networks ◽  
Simple Rules
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