Learning Word Embeddings from Portuguese Lexical-Semantic Knowledge Bases

Abstract

This paper describes the creation of PT-LKB, new Portuguese word embeddings learned from a large lexical-semantic knowledge base (LKB), using the node2vec method. Resulting embeddings combine the strengths of word vector representations and, even with lower dimensions, achieve high scores in genuine similarity, which so far were obtained by exploiting the graph structure of LKBs.