Multi-instance learning of pretopological spaces to model complex propagation phenomena: Application to lexical taxonomy learning

摘要

This paper addresses the problem of learning the concept of propagation in the theoretical formalism of pretopology, and then applying this methodology for the well-known problem of learning Lexical Taxonomy. The theory of pretopology, among others, aims at modeling complex relations between sets of entities. The use of such fine-grained modeling implies limitations in terms of scalability. However, it allows for a more accurate capture of real-world relationships, such as the hypernymy relation, by modeling the task of relation extraction as a propagation model under certain structuring constraints, as opposed to traditional approaches that are limited to detecting relations between pairs of elements without considering knowledge on the expected structuring.