# Building a Web-Scale Dependency-Parsed Corpus from CommonCrawl

arXiv: https://arxiv.org/abs/1710.01779 · HTML: https://mlsanity.com/paper/1710.01779/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Oct 2017.

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

- [From Zero to Hero: Examining the Power of Symbolic Tasks in Instruction Tuning](https://mlsanity.com/paper/2304.07995/) (2023)
- [Limited Generalizability in Argument Mining: State-Of-The-Art Models Learn Datasets, Not Arguments](https://mlsanity.com/paper/2505.22137/) (2025)
- [Parallel Corpora for Machine Translation in Low-resource Indic Languages: A Comprehensive Review](https://mlsanity.com/paper/2503.04797/) (2025)
- [Distributed Marker Representation for Ambiguous Discourse Markers and Entangled Relations](https://mlsanity.com/paper/2306.10658/) (2023)
- [Cross-Genre Argument Mining: Can Language Models Automatically Fill in Missing Discourse Markers?](https://mlsanity.com/paper/2306.04314/) (2023)
- [When Do Discourse Markers Affect Computational Sentence Understanding?](https://mlsanity.com/paper/2309.00368/) (2023)
- [Exploration of Marker-Based Approaches in Argument Mining through Augmented Natural Language](https://mlsanity.com/paper/2406.08606/) (2024)
- [Algebraic Signatures for Structural Learning in Probability Tensors](https://mlsanity.com/paper/2607.18817/) (2026)
- [ThaiTrees: Thai Syntactic Dependency Trees Across Domains](https://mlsanity.com/paper/2609.27558/) (2026)

## Abstract (arXiv)

We present DepCC, the largest-to-date linguistically analyzed corpus in English including 365 million documents, composed of 252 billion tokens and 7.5 billion of named entity occurrences in 14.3 billion sentences from a web-scale crawl of the textscCommon Crawl project. The sentences are processed with a dependency parser and with a named entity tagger and contain provenance information, enabling various applications ranging from training syntax-based word embeddings to open information extraction and question answering. We built an index of all sentences and their linguistic meta-data enabling quick search across the corpus. We demonstrate the utility of this corpus on the verb similarity task by showing that a distributional model trained on our corpus yields better results than models trained on smaller corpora, like Wikipedia. This distributional model outperforms the state of art models of verb similarity trained on smaller corpora on the SimVerb3500 dataset.