# A Greek Parliament Proceedings Dataset for Computational Linguistics and Political Analysis

arXiv: https://arxiv.org/abs/2210.12883 · HTML: https://mlsanity.com/paper/2210.12883/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 51 references that are in the map.
- First posted on arXiv 23 Oct 2022.

## Builds on (most cited first, 22 of 22)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Deep contextualized word representations](https://mlsanity.com/paper/1802.05365/) (2018)
- [Diachronic Word Embeddings Reveal Statistical Laws of Semantic Change](https://mlsanity.com/paper/1605.09096/) (2016)
- [Exploiting Similarities among Languages for Machine Translation](https://mlsanity.com/paper/1309.4168/) (2013)
- [Diachronic word embeddings and semantic shifts: a survey](https://mlsanity.com/paper/1806.03537/) (2018)
- [Analysing Lexical Semantic Change with Contextualised Word Representations](https://mlsanity.com/paper/2004.14118/) (2020)
- [Statistically Significant Detection of Linguistic Change](https://mlsanity.com/paper/1411.3315/) (2014)
- [Cultural Shift or Linguistic Drift? Comparing Two Computational Measures of Semantic Change](https://mlsanity.com/paper/1606.02821/) (2016)
- [Temporal Analysis of Language through Neural Language Models](https://mlsanity.com/paper/1405.3515/) (2014)
- [Explaining and Improving BERT Performance on Lexical Semantic Change Detection](https://mlsanity.com/paper/2103.07259/) (2021)
- [Factors Influencing the Surprising Instability of Word Embeddings](https://mlsanity.com/paper/1804.09692/) (2018)
- [UiO-UvA at SemEval-2020 Task 1: Contextualised Embeddings for Lexical Semantic Change Detection](https://mlsanity.com/paper/2005.00050/) (2020)
- [Training Temporal Word Embeddings with a Compass](https://mlsanity.com/paper/1906.02376/) (2019)
- [Capturing Evolution in Word Usage: Just Add More Clusters?](https://mlsanity.com/paper/2001.06629/) (2020)
- [Simple, Interpretable and Stable Method for Detecting Words with Usage Change across Corpora](https://mlsanity.com/paper/2112.14330/) (2021)
- [KCD: Knowledge Walks and Textual Cues Enhanced Political Perspective Detection in News Media](https://mlsanity.com/paper/2204.04046/) (2022)
- [Statistically significant detection of semantic shifts using contextual word embeddings](https://mlsanity.com/paper/2104.03776/) (2021)
- [Measuring, Predicting and Visualizing Short-Term Change in Word Representation and Usage in VKontakte Social Network](https://mlsanity.com/paper/1703.07012/) (2017)
- [Analyzing the Surprising Variability in Word Embedding Stability Across Languages](https://mlsanity.com/paper/2004.14876/) (2020)
- [A Framework for Quantifying Controversy of Social Network Debates Using Attributed Networks: Biased Random Walk (BRW)](https://mlsanity.com/paper/2008.11453/) (2020)
- [Legislator Representation Learning with Social Context and Expert Knowledge](https://mlsanity.com/paper/2108.03881/) (2021)

## Built on by (most cited first, 4 of 4)

- [A Systematic Survey of Natural Language Processing for the Greek Language](https://mlsanity.com/paper/2407.09861/) (2024)
- [Analyzing German Parliamentary Speeches: A Machine Learning Approach for Topic and Sentiment Classification](https://mlsanity.com/paper/2508.03181/) (2025)
- [Forging GEMs: Advancing Greek NLP through Quality-Based Corpus Curation](https://mlsanity.com/paper/2510.20002/) (2025)
- [Svarna: An Open Corpus Workbench for Modern Greek](https://mlsanity.com/paper/2607.00970/) (2026)

## Abstract (arXiv)

Large, diachronic datasets of political discourse are hard to come across, especially for resource-lean languages such as Greek. In this paper, we introduce a curated dataset of the Greek Parliament Proceedings that extends chronologically from 1989 up to 2020. It consists of more than 1 million speeches with extensive metadata, extracted from 5,355 parliamentary record files. We explain how it was constructed and the challenges that we had to overcome. The dataset can be used for both computational linguistics and political analysis-ideally, combining the two. We present such an application, showing (i) how the dataset can be used to study the change of word usage through time, (ii) between significant historical events and political parties, (iii) by evaluating and employing algorithms for detecting semantic shifts.