The whole web as your dataset

It's a non-profit that makes

web data

freely accessible to anyone

Each crawl archive is billions of pages:


May crawl archive is

2.05 billion web pages

~159 terabytes uncompressed

Released

totally free
without additional
intellectual property restrictions

(lives on Amazon Public Data Sets)

Origins of Common Crawl


Common Crawl founded in 2007
by Gil Elbaz (Applied Semantics / Factual)


Google and Microsoft were the powerhouses

Data powers the algorithms in our field


Goal: Democratize and simplify access to
"the web as a dataset"

Benefits for start-ups & research


Performing your own large-scale crawling is
expensive and challenging


Innovation occurs by using the data,
rarely through novel collection methods


+ We provide raw data, metadata & extracted text
+ Lower the barrier of entry for new start-ups
+ Create a communal pool of knowledge & data

Web Data at Scale

Analytics

+ Usage of servers, libraries, and metadata


Machine Learning

+ Unannotated and semi-supervised methods


Filtering and Aggregation

+ Analyzing tables, phone numbers

    Analytics at Scale


    Imagine you are interested in ...

    Javascript library usage
    + HTML / HTML5 usage
    + Web server types and age

    With Common Crawl you can analyse
    billions of pages in an afternoon!

    Analyzing Web Domain Vulns

    WDC Hyperlink Graph


    Largest freely available real world graph dataset:

    3.5 billion pages, 128 billion links


    Fast and easy analysis using Dato GraphLab on a single EC2 r3.8xlarge instance
    (under 10 minutes per PageRank iteration)

    Web Data at Scale

    Analytics

    + Usage of servers, libraries, and metadata


    Machine Learning

    + Unannotated and semi-supervised methods


    Filtering and Aggregation

    + Analyzing tables, phone numbers

    N-gram Counts & Language Models from the Common Crawl
    Christian Buck, Kenneth Heafield, Bas van Ooyen

    Processed all the text of Common Crawl to produce 975 billion deduplicated tokens
    (similar size to the Google N-gram Dataset)

    Project data was released at
    http://statmt.org/ngrams

    • Deduped text split by language

    • Resulting language models

    GloVe: Global Vectors for Word Representation

    Jeffrey Pennington, Richard Socher, Christopher D. Manning


    Capture "meaning" of words in vector of floats

    king - man + woman = queen

    GloVe and word2vec (Google's word vector method) can scale to hundreds of billions of tokens

    More data => better performance

    king - man + woman = queen

    GloVe On Various Corpora

    • Semantic: "Athens is to Greece as Berlin is to _?" 
    • Syntactic: "Dance is to dancing as fly is to _?" 

    GloVe over Big Data

    Google released a trained word2vec using 100 billion tokens from Google News
    Not replicable


    Stanford released a trained GloVe model using 42 billion & 840 billion tokens from Common Crawl
    Replicable


    Source code and pre-trained models at
    http://www-nlp.stanford.edu/projects/glove/

    Web-Scale Parallel Text

    Dirt Cheap Web-Scale Parallel Text from the Common Crawl (Smith et al.)

    Processed all text from URLs of the style:

    website.com/[langcode]/

    [w.com/en/tesla | w.com/fr/tesla]

    "...nothing more than a set of common two-letter language codes ... [we] mined 32 terabytes ... in just under a day"

    Web-Scale Parallel Text

    Manual inspection across three languages:
    80% of the data contained good translations


    (source = foreign language, target = English)

    Web Data at Scale

    Analytics

    + Usage of servers, libraries, and metadata


    Machine Learning

    + Unannotated and semi-supervised methods


    Filtering and Aggregation

    + Analyzing tables, phone numbers

    Web Data Commons Web Tables

    11.2 billion tables => 147 million relational web tables


    Popular column headers: name, title, artist, location, model, manufacturer, country ...
    Released at webdatacommons.org/webtables/

    Extracting US Phone Numbers


    Task: Match businesses from Yelp's database to their homepages on the Internet using phone numbers.


    Yelp extracted ~748 million US phone numbers from the Common Crawl December 2014 dataset

    Regular expression over extracted text (WET files)

    Extracting US Phone Numbers



    Total complexity:
    134 lines of Python
    Total time: 1 hour (20 × c3.8xlarge)
    Total cost: $10.60 USD (Python using EMR)


    More details (and full code) on Yelp's blog post:
    Analyzing the Web For the Price of a Sandwich

    Why am I so excited..?

    Open data is catching on!

    Even playing field for academia and start-ups


    Common Crawl releases their dataset
    and brilliant people build on top of it


    Read more at
    commoncrawl.org


    Stephen Merity
    stephen@commoncrawl.org
    commoncrawl.org

    The whole web as your dataset

    By smerity

    The whole web as your dataset

    • 1,636
    Loading comments...

    More from smerity