  The relationship between words in a sentence often tells us more about the
underlying semantic content of a document than its actual words, individually.
In this work, we propose two novel algorithms, called Flexible Lexical Chain II
and Fixed Lexical Chain II. These algorithms combine the semantic relations
derived from lexical chains, prior knowledge from lexical databases, and the
robustness of the distributional hypothesis in word embeddings as building
blocks forming a single system. In short, our approach has three main
contributions: (i) a set of techniques that fully integrate word embeddings and
lexical chains; (ii) a more robust semantic representation that considers the
latent relation between words in a document; and (iii) lightweight word
embeddings models that can be extended to any natural language task. We intend
to assess the knowledge of pre-trained models to evaluate their robustness in
the document classification task. The proposed techniques are tested against
seven word embeddings algorithms using five different machine learning
classifiers over six scenarios in the document classification task. Our results
show the integration between lexical chains and word embeddings representations
sustain state-of-the-art results, even against more complex systems.
