Architectures of Meaning, A Systematic Corpus Analysis of NLP Systems

Oskar Wysocki; Malina Florea; Donal Landers; Andre Freitas

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arXiv · 2107.08124

Architectures of Meaning, A Systematic Corpus Analysis of NLP Systems

Oskar Wysocki·Malina Florea·Donal Landers·Andre Freitas

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Abstract

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.

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Oskar Wysocki, Malina Florea, Donal Landers, Andre Freitas. 2021-07-16. Architectures of Meaning, A Systematic Corpus Analysis of NLP Systems. https://arxiv.org/abs/2107.08124

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