Deep recurrent neural network for geographical entities disambiguation on social media data

Abstract

A particular challenge in Natural Language Processing is the disambiguation of polysemic words. The great availability, diversity and the speed of changing of the data from on-line sources force the development of disambiguation systems with a reduced dependency on linguistic resources. We argue that the contextual neural encoding of a specific entity avoids the need of using external linguistic resources like knowledge bases. Hence, we propose a neural network architecture grounded in the use of Long Short-Term Memory Recurrent Neural Network for encoding the context of a target geographical entity, specifically Two k-Contextual Windows model for the disambiguation of the geographical entity Granada. We generate two annotated corpora of texts from social media written in English and Spanish, which we use to evaluate our proposal. The results show that our claim holds.