Blog
The latest news from Google AI
Learning from Big Data: 40 Million Entities in Context
Friday, March 8, 2013
Posted by Dave Orr, Amar Subramanya, and Fernando Pereira, Google Research
When someone mentions Mercury, are they talking about the
planet
, the
god
, the
car
, the
element
,
Freddie
, or one of some
89 other possibilities
? This problem is called
disambiguation
(a word that is itself
ambiguous
), and while it’s necessary for communication, and humans are amazingly good at it (when was the last time you confused a
fruit
with a
giant tech company
?), computers need help.
To provide that help, we are releasing the Wikilinks Corpus: 40 million total disambiguated mentions within over 10 million web pages -- over 100 times bigger than the next largest corpus (about 100,000 documents, see the table below for mention and entity counts). The mentions are found by looking for links to Wikipedia pages where the anchor text of the link closely matches the title of the target Wikipedia page. If we think of each page on Wikipedia as an entity (
an idea we’ve discussed before
), then the anchor text can be thought of as a mention of the corresponding entity.
Dataset
Number of Mentions
Number of Entities
Bentivogli et al.
(
data
) (2008)
43,704
709
Day et al.
(2008)
less than 55,000
3,660
Artiles et al.
(
data
) (2010)
57,357
300
Wikilinks Corpus
40,323,863
2,933,659
What might you do with this data? Well, we’ve already written one
ACL paper on cross-document co-reference
(and received lots of requests for the underlying data, which partly motivates this release). And really, we look forward to seeing what you are going to do with it! But here are a few ideas:
Look into
coreference
-- when different mentions mention the same entity -- or
entity resolution
-- matching a mention to the underlying entity
Work on the bigger problem of
cross-document coreference
, which is how to find out if different web pages are talking about the same person or other entity
Learn things about entities by aggregating information across all the documents they’re mentioned in
Type tagging
tries to assign types (they could be broad, like person, location, or specific, like amusement park ride) to entities. To the extent that the Wikipedia pages contain the type information you’re interested in, it would be easy to construct a training set that annotates the Wikilinks entities with types from Wikipedia.
Work on any of the above, or more, on subsets of the data. With existing datasets, it wasn’t possible to work on just musicians or chefs or train stations, because the sample sizes would be too small. But with 10 million Web pages, you can find a decent sampling of almost anything.
Gory Details
How do you actually get the data? It’s right here:
Google’s Wikilinks Corpus
. Tools and data with extra context can be found on our partners’ page:
UMass Wiki-links
. Understanding the corpus, however, is a little bit involved.
For copyright reasons, we cannot distribute actual annotated web pages. Instead, we’re providing an index of URLs, and the tools to create the dataset, or whichever slice of it you care about, yourself. Specifically, we’re providing:
The URLs of all the pages that contain labeled mentions, which are links to English Wikipedia
The anchor text of the link (the mention string), the Wikipedia link target, and the byte offset of the link for every page in the set
The byte offset of the 10 least frequent words on the page, to act as a signature to ensure that the underlying text hasn’t changed -- think of this as a version, or fingerprint, of the page
Software tools (on the
UMass site
) to: download the web pages; extract the mentions, with ways to recover if the byte offsets don’t match; select the text around the mentions as local context; and compute evaluation metrics over predicted entities.
The format looks like this:
URL http://1967mercurycougar.blogspot.com/2009_10_01_archive.html
MENTION Lincoln Continental Mark IV 40110 http://en.wikipedia.org/wiki/Lincoln_Continental_Mark_IV
MENTION 1975 MGB roadster 41481 http://en.wikipedia.org/wiki/MG_MGB
MENTION Buick Riviera 43316 http://en.wikipedia.org/wiki/Buick_Riviera
MENTION Oldsmobile Toronado 43397 http://en.wikipedia.org/wiki/Oldsmobile_Toronado
TOKEN seen 58190
TOKEN crush 63118
TOKEN owners 69290
TOKEN desk 59772
TOKEN relocate 70683
TOKEN promote 35016
TOKEN between 70846
TOKEN re 52821
TOKEN getting 68968
TOKEN felt 41508
We’d love to hear what you’re working on, and look forward to what you can do with 40 million mentions across over 10 million web pages!
Thanks to our collaborators at
UMass Amherst
:
Sameer Singh
and
Andrew McCallum
.
Labels
accessibility
ACL
ACM
Acoustic Modeling
Adaptive Data Analysis
ads
adsense
adwords
Africa
AI
AI for Social Good
Algorithms
Android
Android Wear
API
App Engine
App Inventor
April Fools
Art
Audio
Augmented Reality
Australia
Automatic Speech Recognition
AutoML
Awards
BigQuery
Cantonese
Chemistry
China
Chrome
Cloud Computing
Collaboration
Compression
Computational Imaging
Computational Photography
Computer Science
Computer Vision
conference
conferences
Conservation
correlate
Course Builder
crowd-sourcing
CVPR
Data Center
Data Discovery
data science
datasets
Deep Learning
DeepDream
DeepMind
distributed systems
Diversity
Earth Engine
economics
Education
Electronic Commerce and Algorithms
electronics
EMEA
EMNLP
Encryption
entities
Entity Salience
Environment
Europe
Exacycle
Expander
Faculty Institute
Faculty Summit
Flu Trends
Fusion Tables
gamification
Gboard
Gmail
Google Accelerated Science
Google Books
Google Brain
Google Cloud Platform
Google Docs
Google Drive
Google Genomics
Google Maps
Google Photos
Google Play Apps
Google Science Fair
Google Sheets
Google Translate
Google Trips
Google Voice Search
Google+
Government
grants
Graph
Graph Mining
Hardware
HCI
Health
High Dynamic Range Imaging
ICCV
ICLR
ICML
ICSE
Image Annotation
Image Classification
Image Processing
Inbox
India
Information Retrieval
internationalization
Internet of Things
Interspeech
IPython
Journalism
jsm
jsm2011
K-12
Kaggle
KDD
Keyboard Input
Klingon
Korean
Labs
Linear Optimization
localization
Low-Light Photography
Machine Hearing
Machine Intelligence
Machine Learning
Machine Perception
Machine Translation
Magenta
MapReduce
market algorithms
Market Research
Mixed Reality
ML
ML Fairness
MOOC
Moore's Law
Multimodal Learning
NAACL
Natural Language Processing
Natural Language Understanding
Network Management
Networks
Neural Networks
NeurIPS
Nexus
Ngram
NIPS
NLP
On-device Learning
open source
operating systems
Optical Character Recognition
optimization
osdi
osdi10
patents
Peer Review
ph.d. fellowship
PhD Fellowship
PhotoScan
Physics
PiLab
Pixel
Policy
Professional Development
Proposals
Public Data Explorer
publication
Publications
Quantum AI
Quantum Computing
Recommender Systems
Reinforcement Learning
renewable energy
Research
Research Awards
resource optimization
Robotics
schema.org
Search
search ads
Security and Privacy
Self-Supervised Learning
Semantic Models
Semi-supervised Learning
SIGCOMM
SIGMOD
Site Reliability Engineering
Social Networks
Software
Sound Search
Speech
Speech Recognition
statistics
Structured Data
Style Transfer
Supervised Learning
Systems
TensorBoard
TensorFlow
TPU
Translate
trends
TTS
TV
UI
University Relations
UNIX
Unsupervised Learning
User Experience
video
Video Analysis
Virtual Reality
Vision Research
Visiting Faculty
Visualization
VLDB
Voice Search
Wiki
wikipedia
WWW
Year in Review
YouTube
Archive
2020
Mar
Feb
Jan
2019
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2018
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2017
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2016
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2015
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2014
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2013
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2012
Dec
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2011
Dec
Nov
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2010
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2009
Dec
Nov
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2008
Dec
Nov
Oct
Sep
Jul
May
Apr
Mar
Feb
2007
Oct
Sep
Aug
Jul
Jun
Feb
2006
Dec
Nov
Sep
Aug
Jul
Jun
Apr
Mar
Feb
Feed
Follow @googleai
Give us feedback in our
Product Forums
.
