Semantic Cells: Evolutional Process to Acquire Sense Diversity of Items

TL;DR

This paper introduces a dynamic model for semantic vectors that evolve through interactions, capturing sense diversity and contextual shifts, with preliminary results linking semantic variance to authorial influence and earthquake epicenters.

Contribution

The paper proposes a novel evolutional process for semantic vectors that adapt dynamically through interactions, unlike traditional static models.

Findings

Semantic vectors evolve via interaction, capturing sense diversity.

Words with higher semantic variance are linked to authorial influence.

Larger variance in seismic epicenters correlates with future earthquakes.

Abstract

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.