  Previous models for learning the semantic vectors of items and their groups,
such as words, sentences, nodes, and graphs, using distributed representation
have been based on the assumption that the basic sense of an item corresponds
to one vector composed of dimensions corresponding to hidden contexts in the
target real world, from which multiple senses of the item are obtained by
conforming to lexical databases or adapting to the context. However, there may
be multiple senses of an item, which are hardly assimilated and change or
evolve dynamically following the contextual shift even within a document or a
restricted period. This is a process similar to the evolution or adaptation of
a living entity with/to environmental shifts. Setting the scope of
disambiguation of items for sensemaking, the author presents a method in which
a word or item in the data embraces multiple semantic vectors that evolve via
interaction with others, similar to a cell embracing chromosomes crossing over
with each other. We obtained two preliminary results: (1) the role of a word
that evolves to acquire the largest or lower-middle variance of semantic
vectors tends to be explainable by the author of the text; (2) the epicenters
of earthquakes that acquire larger variance via crossover, corresponding to the
interaction with diverse areas of land crust, are likely to correspond to the
epicenters of forthcoming large earthquakes.
