Internet Scale Analytics
With Common Crawl

It's a non-profit that makes

web data

freely accessible to anyone

Each crawl archive is billions of pages:


February crawl archive is

1.9 billion web pages

~154 terabytes uncompressed

Released

totally free
without additional
intellectual property restrictions
(lives on Amazon Public Data Sets)

Common Crawl File Formats

  • WARC
    + Raw HTTP response headers
    + Raw HTTP responses

  • WAT
    + HTML head data
    + HTTP header fields
    + Extracted links / script tags

  • WET
    + Extracted text

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"

Tackling the Web as a Dataset


The web is largely unannotated,

so how are people using it?

(a) Use data for unsupervised algorithms / analysis

 (b) Filter it into being semi-annotated or annotated
(big data ⇒ filter ⇒ curated smaller dataset)

Web Data at Scale

Analytics

+ Usage of servers, libraries, and metadata


Machine Learning

+ Language models based upon billions of tokens


Filtering and Aggregation

+ Analyzing tables, Wikipedia, and phone numbers

    Analytics at Scale


    If you have an afternoon and are interested in ...

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

    You can immediately get started over billions of pages!

    Analyzing Web Domain Vulns



    1) How many websites use Google Analytics (GA)?


    2) How much of a user's browsing history is captured by Google Analytics?

    Impact of Google Analytics

    Top 10k domains:

    65.7%


    Top 100k domains:

    64.2%


    Top million domains:

    50.8%

    Impact of Google Analytics



    WDC Hyperlink Graph


    Largest freely available real world graph dataset:

    3.6 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

    + Language models based upon billions of tokens


    Filtering and Aggregation

    + Analyzing tables, Wikipedia, and phone numbers

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


    N-grams = How many times did a phrase appear?


    Processed all the text of Common Crawl to produce 975 billion deduplicated tokens

    Google N-gram Dataset (Web 1T) consists of
    1 trillion tokens

    N-gram Counts & Language Models from the Common Crawl


    Improvement over Google N-grams (2006):
    • Inclusion of low count entries 
    • Deduplication to reduce boilerplate

    "Google has shared a deduplicated version ...
    in limited contexts, but it was never publicly released."
    -- N-gram Counts & Language Models from the Common Crawl (Buck et al.)

    N-gram Counts & Language Models from the Common Crawl


    English (23TB), German (1.02TB), Spanish (986GB), French (912GB), Japanese (577GB), Russian (537GB), Polish (334GB), Italian (325GB) ...

    Only 0.14% of the corpus was Finnish, yet yielded a useful corpus of 47GB.

    42 languages with >10GB

    73 languages with >1GB

    N-gram & Language Models

    "...even though the web data is quite noisy even limited amounts give improvements."


    N-gram & Language Models


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

    • Raw text split by language

    • Deduped text split by language

    • Resulting language models

    GloVe: Global Vectors for Word Representation

    Jeffrey Pennington, Richard Socher, Christopher D. Manning


    Word vector representations:

    king - queen = man - woman

    king - man + woman = queen

    (produces dimensions of meaning)



    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

    GloVe and word2vec (competing algorithm) can scale to hundreds of billions of tokens


    Best of all: the performance keeps improving


    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



    (source = foreign language, target = English)

    Web-Scale Parallel Text


    Both EuroParl & United Nations are large and well curated parallel texts,

    but both have very specific domains & genres.


    Web-Scale Parallel Text


    "...resulting in improvements of up to 1.5 BLEU on standard test sets, and 5 BLEU on test sets outside of the news domain."

    Minimal cleaning & filtering still resulted in a substantial improvement in SMT performance


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



    Web Data at Scale

    Analytics

    + Usage of servers, libraries, and metadata


    Machine Learning

    + Language models based upon billions of tokens


    Filtering and Aggregation

    + Analyzing tables, Wikipedia, and phone numbers

    Gazetteers via "Google Sets"


    Idea: Web tables for gazetteers + relations


    Querying ["cat"],

    returns ["dog", "bird", "horse", "rabbit", ...]

    Querying ["cat", "ls"],

    returns ["cd", "head", "cut", "vim", ...]


    Web Data Commons Web Tables


    Extracted 11.2 billion tables from WARC files,
    filtered to keep relational tables via trained classifier


    Only 1.3% of the original data was kept,
    yet it still remains hugely valuable


    Resulting dataset:
    11.2 billion tables ⇒ 147 million relational web tables

    Web Data Commons Web Tables


    Popular column headers: name, title, artist, location, model, manufacturer, country ...

    Released at webdatacommons.org/webtables/

    Web Data Commons Web Tables

    Extracting US Phone Numbers


    "Let's use Common Crawl to help match businesses from Yelp's database to the possible web pages for those businesses on the Internet."


    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)

    Matched against Yelp's database:
    • 48% had exact URL matches
    • 61% had matching domains

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

    Common Crawl's Derived Datasets


    Natural language processing:

    Large scale web analysis:

    and a million more use cases!

    Why am I so excited..?

    Open data is catching on!

    Even playing field for academia and industry


    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

    Internet Scale Analytics With Common Crawl

    By smerity

    Made with Slides.com

    Internet Scale Analytics With Common Crawl

    • 4,572

    More from smerity