  This paper proposes a novel statistical corpus analysis framework targeted
towards the interpretation of Natural Language Processing (NLP) architectural
patterns at scale. The proposed approach combines saturation-based lexicon
construction, statistical corpus analysis methods and graph collocations to
induce a synthesis representation of NLP architectural patterns from corpora.
The framework is validated in the full corpus of Semeval tasks and demonstrated
coherent architectural patterns which can be used to answer architectural
questions on a data-driven fashion, providing a systematic mechanism to
interpret a largely dynamic and exponentially growing field.
