  Entity Recognition (ER) within a text is a fundamental exercise in Natural
Language Processing, enabling further depending tasks such as Knowledge
Extraction, Text Summarisation, or Keyphrase Extraction. An entity consists of
single words or of a consecutive sequence of terms, constituting the basic
building blocks for communication. Mainstream ER approaches are mainly limited
to flat structures, concentrating on the outermost entities while ignoring the
inner ones. This paper introduces a partly-layered network architecture that
deals with the complexity of overlapping and nested cases. The proposed
architecture consists of two parts: (1) a shared Sequence Layer and (2) a
stacked component with multiple Tagging Layers. The adoption of such an
architecture has the advantage of preventing overfit to a specific word-length,
thus maintaining performance for longer entities despite their lower frequency.
To verify the proposed architecture's effectiveness, we train and evaluate this
architecture to recognise two kinds of entities - Concepts (CR) and Named
Entities (NER). Our approach achieves state-of-the-art NER performances, while
it outperforms previous CR approaches. Considering these promising results, we
see the possibility to evolve the architecture for other cases such as the
extraction of events or the detection of argumentative components.
