A cognitively driven weighted-entropy model for embedding semantic categories in hyperbolic geometry

In this paper, an unsupervised and cognitively driven weighted-entropy method for embedding semantic categories in hyperbolic geometry is proposed. The model is driven by two fields of research in cognitive linguistics: the first is the statistical learning theory of language acquisition and the proposal of using high-dimensional networks to represent semantic knowledge in cognition, and the second is the domain-specific informativeness approach to semantic communication. Weighted conditional entropy of word co-occurrence is proposed as the embedding metric, and the two weighting parameters are collocation diversity and conditional probability ranking in the corresponding statistical distribution. The Boltzmann distribution is then used on the weighted-entropy metric and embedded into a hyperbolic Poincare disk model. Testing has been mainly performed in the domains of basic color and kinship words, which belong to the classes that domain-specificity focused research in cognitive semantics has most intensively investigated. Results show that this new approach can successfully model and map the semantic relationships of popularity and similarity for most of the basic color and kinship words in English and have potential to be generalized to other semantic domains and different languages. Generally, this paper contributes to both computational cognitive semantics and the research on network and geometry-driven language embedding in computational linguistics and NLP.