# AMALGUM -- A Free, Balanced, Multilayer English Web Corpus

arXiv: https://arxiv.org/abs/2006.10677 · HTML: https://mlsanity.com/paper/2006.10677/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Jun 2020.

## Built on by (most cited first, 9 of 9)

- [DisCoDisCo at the DISRPT2021 Shared Task: A System for Discourse Segmentation, Classification, and Connective Detection](https://mlsanity.com/paper/2109.09777/) (2021)
- [Why Can't Discourse Parsing Generalize? A Thorough Investigation of the Impact of Data Diversity](https://mlsanity.com/paper/2302.06488/) (2023)
- [eRST: A Signaled Graph Theory of Discourse Relations and Organization](https://mlsanity.com/paper/2403.13560/) (2024)
- [GDTB: Genre Diverse Data for English Shallow Discourse Parsing across Modalities, Text Types, and Domains](https://mlsanity.com/paper/2411.00491/) (2024)
- [Aggregating Crowdsourced and Automatic Judgments to Scale Up a Corpus of Anaphoric Reference for Fiction and Wikipedia Texts](https://mlsanity.com/paper/2210.05581/) (2022)
- [Unintended Memorization and Timing Attacks in Named Entity Recognition Models](https://mlsanity.com/paper/2211.02245/) (2022)
- [What's Hard in English RST Parsing? Predictive Models for Error Analysis](https://mlsanity.com/paper/2309.04940/) (2023)
- [Syntactic Inductive Bias in Transformer Language Models: Especially Helpful for Low-Resource Languages?](https://mlsanity.com/paper/2311.00268/) (2023)
- [Unpacking Ambiguity: The Interaction of Polysemous Discourse Markers and Non-DM Signals](https://mlsanity.com/paper/2507.16748/) (2025)

## Abstract (arXiv)

We present a freely available, genre-balanced English web corpus totaling 4M tokens and featuring a large number of high-quality automatic annotation layers, including dependency trees, non-named entity annotations, coreference resolution, and discourse trees in Rhetorical Structure Theory. By tapping open online data sources the corpus is meant to offer a more sizable alternative to smaller manually created annotated data sets, while avoiding pitfalls such as imbalanced or unknown composition, licensing problems, and low-quality natural language processing. We harness knowledge from multiple annotation layers in order to achieve a "better than NLP" benchmark and evaluate the accuracy of the resulting resource.