# Architectures of Meaning, A Systematic Corpus Analysis of NLP Systems

arXiv: https://arxiv.org/abs/2107.08124 · HTML: https://mlsanity.com/paper/2107.08124/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 17 references that are in the map.
- First posted on arXiv 16 Jul 2021.

## Builds on (most cited first, 3 of 3)

- [Deep Learning for Sentiment Analysis : A Survey](https://mlsanity.com/paper/1801.07883/) (2018)
- [The State of NLP Literature: A Diachronic Analysis of the ACL Anthology](https://mlsanity.com/paper/1911.03562/) (2019)
- [What is SemEval evaluating? A Systematic Analysis of Evaluation Campaigns in NLP](https://mlsanity.com/paper/2005.14299/) (2020)

## Abstract (arXiv)

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.