Deep learning for social sensing from tweets


Distributional Semantic Models (DSM) that represent words as vectors of weights over a high dimensional feature space have proved very effective in representing semantic or syntactic word similarity. For certain tasks however it is important to represent contrasting aspects such as polarity, opposite senses or idiomatic use of words. We present a method for computing discriminative word embeddings can be used in sentiment classification or any other task where one needs to discriminate between con-trasting semantic aspects. We present an experiment in the identification of reports on natural disasters in tweets by means of these embeddings.

In Proceedings of the Second Italian Conference on Computational Linguistics (CLiC-it 2015, Trento)
Alessio Miaschi
Alessio Miaschi
PhD Candidate in Computer Science