In this paper, we review methods of textual representation from two ways: one by topic representation, obtaining vectors that encode the pertinence of words in sentences if they appear together in a corpus, and one by word embeddings, creating word representations based on their occurrence context and combining them in a single document vector.
the methods for textual representation investigated in this paper are: • Latent Semantic Indexing (LSI) (Landauer et al., 1998): This method uses the term-document matrix that encodes the frequency of each term by document and its eigenvalues and eigenvectors to decompose this data and find a new representation.