A Systematic Survey of Natural Language Processing for the Greek Language
This provides a systematic approach for assessing NLP in not well-resourced languages like Greek, though it is incremental as it adapts existing survey concepts to a specific domain.
The study tackled the lack of standardized methodologies in monolingual NLP surveys by introducing a generalizable framework to minimize bias and classify tasks and resources, applying it to Greek NLP from 2012-2023 to analyze its state and gaps, with results publicly available and regularly updated.
Comprehensive monolingual Natural Language Processing (NLP) surveys are essential for assessing language-specific challenges, resource availability, and research gaps. However, existing surveys often lack standardized methodologies, leading to selection bias and fragmented coverage of NLP tasks and resources. This study introduces a generalizable framework for systematic monolingual NLP surveys. Our approach integrates a structured search protocol to minimize bias, an NLP task taxonomy for classification, and language resource taxonomies to identify potential benchmarks and highlight opportunities for improving resource availability. We apply this framework to Greek NLP (2012-2023), providing an in-depth analysis of its current state, task-specific progress, and resource gaps. The survey results are publicly available (https://doi.org/10.5281/zenodo.15314882) and are regularly updated to provide an evergreen resource. This systematic survey of Greek NLP serves as a case study, demonstrating the effectiveness of our framework and its potential for broader application to other not so well-resourced languages as regards NLP.