Jump to Content

Defining the technology of today and tomorrow.

Philosophy

We strive to create an environment conducive to many different types of research across many different time scales and levels of risk.
Learn more about our Philosophy

Philosophy

People

Our researchers drive advancements in computer science through both fundamental and applied research.
Learn more about our People

People
Research areas

Explore all research areas

Explore all research areas

Foundational ML & Algorithms

Algorithms & Theory

Data Management

Data Mining & Modeling

Information Retrieval & the Web

Machine Intelligence

Machine Perception

Machine Translation

Natural Language Processing

Speech Processing

Algorithms & Theory

Data Management

Data Mining & Modeling

Information Retrieval & the Web

Machine Intelligence

Machine Perception

Machine Translation

Natural Language Processing

Speech Processing

Computing Systems & Quantum AI

Distributed Systems & Parallel Computing

Hardware & Architecture

Mobile Systems

Networking

Quantum Computing

Robotics

Security, Privacy, & Abuse Prevention

Software Engineering

Software Systems

Distributed Systems & Parallel Computing

Hardware & Architecture

Mobile Systems

Networking

Quantum Computing

Robotics

Security, Privacy, & Abuse Prevention

Software Engineering

Software Systems

Science, AI & Society

Climate & Sustainability

Economics & Electronic Commerce

Education Innovation

General Science

Health & Bioscience

Human-Computer Interaction and Visualization

Climate & Sustainability

Economics & Electronic Commerce

Education Innovation

General Science

Health & Bioscience

Human-Computer Interaction and Visualization
Projects

We regularly open-source projects with the broader research community and apply our developments to Google products.
Learn more about our Projects

Projects

Publications

Publishing our work allows us to share ideas and work collaboratively to advance the field of computer science.
Learn more about our Publications

Publications

Resources

We make products, tools, and datasets available to everyone with the goal of building a more collaborative ecosystem.
Learn more about our Resources

Resources
Shaping the future, together.
Collaborate with us

Student programs

Supporting the next generation of researchers through a wide range of programming.
Learn more about our Student programs

Student programs

Faculty programs

Participating in the academic research community through meaningful engagement with university faculty.
Learn more about our Faculty programs

Faculty programs

Conferences & events

Connecting with the broader research community through events is essential for creating progress in every aspect of our work.
Learn more about our Conferences & events

Conferences & events

Collaborate with us
Careers
Blog

CoNLL 2017 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies

Daniel Zeman

Martin Popel

Milan Straka

Jan Hajic

Joakim Nivre

Filip Ginter

Juhani Luotolahti

Sampo Pyysalo

Slav Petrov

Martin Potthast

Francis Tyers

Elena Badmaeva

Memduh Gokırmak

Anna Nedoluzhko

Silvie Cinkova

Jan Hajic jr.

Jaroslava Hlava

Vaclava Kettnerov

Zdenka Ure

Jenna Kanerva

Stina Ojala

Anna Missil

Christopher Manning

Sebastian Schuster

Siva Reddy

Dima Taji

Nizar Habash

Herman Leung

Marie-Catherine de Marneffe

Manuela Sanguinetti

Maria Simi

Hiroshi Kanayama

Valeria de Paiva

Kira Droganova

Hector Martinez Alonso,

Çağrı Çöltekin

Umut Sulubacak

Hans Uszkoreit

Vivien Macketanz

Aljoscha Burchardt

Kim Harris Katrin Marheinecke

Georg Rehm

Tolga Kayadelen

Mohammed Attia

Ali Elkahky

Zhuoran Yu

Emily Pitler

Saran Lertpradit

Michael Mandl

Jesse Kirchner

Hector Fernandez Alcalde

Jana Strnadova

Esha Banerjee

Ruli Manurung

Antonio Stella

Atsuko Shimada

Sookyoung Kwak

Gustavo Mendonça

Tatiana Lando

Rattima Nitisaroj

Josie Li

Proceedings of the CoNLL 2017 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies (2017), pp. 1-19

Download Google Scholar

Abstract

Research Areas

Natural Language Processing