  This study is a preliminary exploration of the concept of informativeness
-how much information a sentence gives about a word it contains- and its
potential benefits to building quality word representations from scarce data.
We propose several sentence-level classifiers to predict informativeness, and
we perform a manual annotation on a set of sentences. We conclude that these
two measures correspond to different notions of informativeness. However, our
experiments show that using the classifiers' predictions to train word
embeddings has an impact on embedding quality.
