L2 Syntactic Complexity Analyzer - définition. Qu'est-ce que L2 Syntactic Complexity Analyzer
Diclib.com
Dictionnaire ChatGPT
Entrez un mot ou une phrase dans n'importe quelle langue 👆
Langue:

Traduction et analyse de mots par intelligence artificielle ChatGPT

Sur cette page, vous pouvez obtenir une analyse détaillée d'un mot ou d'une phrase, réalisée à l'aide de la meilleure technologie d'intelligence artificielle à ce jour:

  • comment le mot est utilisé
  • fréquence d'utilisation
  • il est utilisé plus souvent dans le discours oral ou écrit
  • options de traduction de mots
  • exemples d'utilisation (plusieurs phrases avec traduction)
  • étymologie

Qu'est-ce (qui) est L2 Syntactic Complexity Analyzer - définition


L2 Syntactic Complexity Analyzer         
L2 Syntactical Complexity Analyzer (L2SCA) developed by Xiaofei Lu at the Pennsylvania State University, is a computational tool which produces syntactic complexity indices of written English language texts. Along with Coh-Metrix, the L2SCA is one of the most extensively used computational tool to compute indices of second language writing development.
Computational complexity         
MEASURE OF THE AMOUNT OF RESOURCES NEEDED TO RUN AN ALGORITHM OR SOLVE A COMPUTATIONAL PROBLEM
Asymptotic complexity; Computational Complexity; Bit complexity; Context of computational complexity; Complexity of computation (bit); Computational complexities
In computer science, the computational complexity or simply complexity of an algorithm is the amount of resources required to run it. Particular focus is given to computation time (generally measured by the number of needed elementary operations) and memory storage requirements.
complexity         
PROFESSIONAL ESPORTS ORGANIZATION BASED IN THE UNITED STATES
Los Angeles Complexity; CompLexity Gaming; LA Complexity; Complexity LA; CompLexity; Team CompLexity; CoL.Black; CoL
<algorithm> The level in difficulty in solving mathematically posed problems as measured by the time, number of steps or arithmetic operations, or memory space required (called time complexity, computational complexity, and space complexity, respectively). The interesting aspect is usually how complexity scales with the size of the input (the "scalability"), where the size of the input is described by some number N. Thus an algorithm may have computational complexity O(N^2) (of the order of the square of the size of the input), in which case if the input doubles in size, the computation will take four times as many steps. The ideal is a constant time algorithm (O(1)) or failing that, O(N). See also NP-complete. (1994-10-20)