  The "ego network of words" model captures structural properties in language
production associated with cognitive constraints. While previous research
focused on the layer-based structure and its semantic properties, this paper
argues that an essential element, the concept of an active network, is missing.
The active part of the ego network of words only includes words that are
regularly used by individuals, akin to the ego networks in the social domain,
where the active part includes relationships regularly nurtured by individuals
and hence demanding cognitive effort. In this work, we define a methodology for
extracting the active part of the ego network of words and validate it using
interview transcripts and tweets. The robustness of our method to varying input
data sizes and temporal stability is demonstrated. We also demonstrate that
without the active network concept (and a tool for properly extracting the
active network from data), the "ego network of words" model is not able to
properly estimate the cognitive effort involved and it becomes vulnerable to
the amount of data considered (leading to the disappearance of the layered
structure in large datasets). Our results are well-aligned with prior analyses
of the ego network of words, where the limitation of the data collected led
automatically (and implicitly) to approximately consider the active part of the
network only. Moreover, the validation on the transcripts dataset (MediaSum)
highlights the generalizability of the model across diverse domains and the
ingrained cognitive constraints in language usage.
