Blog
The latest news from Google AI
A Multilingual Corpus of Automatically Extracted Relations from Wikipedia
Tuesday, June 2, 2015
Posted by Shankar Kumar, Google Research Scientist and Manaal Faruqui, Carnegie Mellon University PhD candidate
In
Natural Language Processing
, relation extraction is the task of assigning a semantic relationship between a pair of arguments. As an example, a relationship between the phrases “
Ottawa
” and “
Canada
” is “
is the capital of
”. These extracted relations could be used in a variety of applications ranging from
Question Answering
to building databases from unstructured text.
While relation extraction systems work accurately for English and a few other languages, where tools for syntactic analysis such as parsers, part-of-speech taggers and named entity analyzers are readily available, there is relatively little work in developing such systems for most of the world's languages where linguistic analysis tools do not yet exist. Fortunately, because we do have translation systems between English and many other languages (such as
Google Translate
), we can translate text from a non-English language to English, perform relation extraction and project these relations back to the foreign language.
Relation extraction in a Spanish sentence using the cross-lingual relation extraction pipeline.
In
Multilingual Open Relation Extraction Using Cross-lingual Projection
, that will appear at the
2015 Conference of the North American Chapter of the Association for Computational Linguistics – Human Language Technologies
(NAACL HLT 2015), we use this idea of cross-lingual projection to develop an algorithm that extracts open-domain relation
tuples
, i.e. where an arbitrary phrase can describe the relation between the arguments, in multiple languages from
Wikipedia
. In this work, we also evaluated the performance of extracted relations using human annotations in French, Hindi and Russian.
Since there is no such publicly available corpus of multilingual relations, we are
releasing a dataset
of automatically extracted relations from the Wikipedia corpus in 61 languages, along with the manually annotated relations in 3 languages (French, Hindi and Russian). It is our hope that our data will help researchers working on natural language processing and encourage novel applications in a wide variety of languages.
We wish to thank Bruno Cartoni, Vitaly Nikolaev, Hidetoshi Shimokawa, Kishore Papineni, John Giannandrea and their teams for making this data release possible. This dataset is licensed by Google Inc. under the
Creative Commons Attribution-ShareAlike 3.0 License
.
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
Jul
Jun
May
Apr
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
.
