Building a Web-Scale Dependency-Parsed Corpus from CommonCrawl
This provides a web-scale parsed corpus for NLP researchers, though it's incremental as it applies existing methods to new data.
The authors created DepCC, the largest linguistically analyzed English corpus with 365 million documents and 252 billion tokens, processed with dependency parsing and named entity tagging. They demonstrated its utility by training a distributional model that outperforms state-of-the-art models on the SimVerb3500 verb similarity dataset.
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 \textsc{Common 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.