Enhancing the LexVec Distributed Word Representation Model Using   Positional Contexts and External Memory
  In this paper we take a state-of-the-art model for distributed word representation that explicitly factorizes the positive pointwise mutual information (PPMI) matrix using window sampling and negative sampling and address two of its shortcomings. We improve syntactic performance by using positional contexts, and solve the need to store the PPMI matrix in memory by working on aggregate data in external memory. The effectiveness of both modifications is shown using word similarity and analogy tasks. 
Alexandre Salle and Marco Idiart and Aline Villavicencio
cs.CL