AMALGUM:

AMALGUM is a machine annotated multilayer corpus following the same design and annotation layers as GUM, but substantially larger (around 4M tokens). The goal of this corpus is to close the gap between high quality, richly annotated, but small datasets, and the larger but shallowly annotated corpora that are often scraped from the Web. In particular, we aim to make data available to support:

• Pretraining on large scale, silver quality data before fine tuning on smaller gold standard datasets
• Active learning to supplement training data and iteratively improve AMALGUM's own data
• Better-than-out-of-the-box quality NLP, using every possible trick as a tool and a target for NLP research

Composition

AMALGUM follows the same corpus design as GUM and currently contains the text types from the GUM version 6 series, with some different sources to allow for the larger scale:

Text type	Source		Docs	Tokens
Interviews	Wikinews		778	500,090
News stories	Wikinews		778	500,090
Travel guides	Wikivoyage		482	500,680
How-to guides	wikiHow		613	500,014
Academic writing	MDPI		662	500,285
Biographies	Wikipedia		600	500,760
Fiction	Project Gutenberg		457	500,088
Forum discussions	reddit		682	500,412
Total			4,960	4,002,929