NL2

nL2 is an acronym that appears in several disciplines and does not have a single universal meaning. In published literature it may refer to normalized L2 constructs in mathematics and machine learning, or to non-native or second-language topics in linguistics, among other uses. Because the term is field-specific, its interpretation should be inferred from context and the definitions provided by authors.

In mathematics and machine learning settings, nL2 most commonly denotes a normalized version of the L2 norm or a normalized L2 regularization term. The L2 norm of a vector is the square root of the sum of squares of its components. A normalized L2 penalty may divide the squared weights by a factor such as the number of parameters, samples, or a dataset-specific constant, yielding a scale-invariant objective. This can improve convergence stability when comparing models of different sizes or trained on datasets of varying scale. Some formulations define the penalty as lambda multiplied by the sum of squared weights divided by a normalizing factor, such as the number of parameters or samples.

In linguistics and second-language acquisition, nL2 is less standardized but appears in some papers as shorthand for non-native or newly learned second-language states or for tasks involving non-native language users. It is not broadly adopted as a formal term, and readers should check the explicit definition in each work.

Overall, nL2 is a context-dependent term. Readers should consult the specific source to understand its intended meaning in that context, particularly when comparing across fields. See also: L1, L2, second language acquisition, regularization.