Blog
The latest news from Google AI
Building resources to syntactically parse the web
Wednesday, March 9, 2011
Posted by Slav Petrov and Ryan McDonald, Research Team
One major hurdle in organizing the world’s information is building computer systems that can understand natural, or human, language. Such understanding would advance if systems could automatically determine syntactic and semantic structures.
This analysis is an extremely complex inferential process. Consider for example the sentence, "A hearing is scheduled on the issue today." A syntactic parser needs to determine that "is scheduled" is a verb phrase, that the "hearing" is its subject, that the prepositional phrase "on the issue" is modifying the "hearing", and that today is an adverb modifying the verb phrase. Of course, humans do this all the time without realizing it. For computers, this is non-trivial as it requires a fair amount of background knowledge, typically encoded in a rich statistical model. Consider, "I saw a man with a jacket" versus "I saw a man with a telescope". In the former, we know that a "jacket" is something that people wear and is not a mechanism for viewing people. So syntactically, the "jacket" must be a property associated with the "man" and not the verb "saw", i.e., I did not see the man by using a jacket to view him. Whereas in the latter, we know that a telescope is something with which we can view people, so it can also be a property of the verb. Of course, it is ambiguous, maybe the man is carrying the telescope.
Linguistically inclined readers will of course notice that this parse tree has been simplified by omitting empty clauses and traces.
Computer programs with the ability to analyze the syntactic structure of language are fundamental to improving the quality of many tools millions of people use every day, including
machine translation
,
question answering
,
information extraction
, and
sentiment analysis
. Google itself is already using syntactic parsers in many of its projects. For example,
this paper
, describes a system where a syntactic dependency parser is used to make translations more grammatical between languages with different word orderings.
This paper
uses the output of a syntactic parser to help determine the scope of negation within sentences, which is then used downstream to improve a
sentiment
analysis
system
.
To further this work, Google is pleased to announce a gift to the
Linguistic Data Consortium (LDC)
to create new annotated resources that can facilitate research progress in the area of syntactic parsing. The primary purpose of the gift is to generate data sets that language technology researchers can use to evaluate the robustness of new parsing methods in several web domains, such as blogs and discussion forums. The goal is to move parsing beyond its current focus on carefully edited text such as print news (for which annotated resources already exist) to domains with larger stylistic and topical variability (where spelling errors and grammatical mistakes are more common).
The Linguistic Data Consortium is a non-profit organization that produces and distributes linguistic data to researchers, technology developers, universities and university libraries. The LDC is hosted by the University of Pennsylvania and directed by Mark Liberman, Christopher H. Browne Distinguished Professor of Linguistics.
The LDC is the leader in building linguistic data resources and will annotate several thousand sentences with syntactic parse trees like the one shown in the figure. The annotation will be done manually by specially trained linguists who will also have access to machine analysis and can correct errors the systems make. Once the annotation is completed, the corpus will be released to the research community through the
LDC catalog
. We look forward to seeing what they produce and what the natural language processing research community can do with the rich annotation resource.
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
2021
Feb
Jan
2020
Dec
Nov
Oct
Sep
Aug
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
.
