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The CoNLL 2008 Shared Task on Joint Parsing of Syntactic and Semantic Dependencies

TLDR
This shared task not only unifies the shared tasks of the previous four years under a unique dependency-based formalism, but also extends them significantly: this year's syntactic dependencies include more information such as named-entity boundaries; the semantic dependencies model roles of both verbal and nominal predicates.
Abstract
The Conference on Computational Natural Language Learning is accompanied every year by a shared task whose purpose is to promote natural language processing applications and evaluate them in a standard setting. In 2008 the shared task was dedicated to the joint parsing of syntactic and semantic dependencies. This shared task not only unifies the shared tasks of the previous four years under a unique dependency-based formalism, but also extends them significantly: this year's syntactic dependencies include more information such as named-entity boundaries; the semantic dependencies model roles of both verbal and nominal predicates. In this paper, we define the shared task and describe how the data sets were created. Furthermore, we report and analyze the results and describe the approaches of the participating systems.

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Citations
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Journal ArticleDOI

A Survey of the Usages of Deep Learning for Natural Language Processing

TL;DR: The field of natural language processing has been propelled forward by an explosion in the use of deep learning models over the last several years as mentioned in this paper, which includes several core linguistic processing issues in addition to many applications of computational linguistics.
Proceedings Article

CoNLL-2012 Shared Task: Modeling Multilingual Unrestricted Coreference in OntoNotes

TL;DR: The OntoNotes annotation (coreference and other layers) is described and the parameters of the shared task including the format, pre-processing information, evaluation criteria, and presents and discusses the results achieved by the participating systems.
Proceedings ArticleDOI

Graph Convolutional Encoders for Syntax-aware Neural Machine Translation

TL;DR: The authors proposed a simple and effective approach to incorporate syntactic structure into neural attention-based encoder-decoder models for machine translation by using graph convolutional networks (GCNs).
Book

Dependency Parsing

TL;DR: This book surveys the three major classes of parsing models that are in current use: transition- based, graph-based, and grammar-based models, and gives a thorough introduction to the methods that are most widely used today.
References
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Journal ArticleDOI

WordNet : an electronic lexical database

Christiane Fellbaum
- 01 Sep 2000 - 
TL;DR: The lexical database: nouns in WordNet, Katherine J. Miller a semantic network of English verbs, and applications of WordNet: building semantic concordances are presented.
ReportDOI

Building a large annotated corpus of English: the penn treebank

TL;DR: As a result of this grant, the researchers have now published on CDROM a corpus of over 4 million words of running text annotated with part-of- speech (POS) tags, which includes a fully hand-parsed version of the classic Brown corpus.
Proceedings ArticleDOI

Introduction to the CoNLL-2003 shared task: language-independent named entity recognition

TL;DR: The CoNLL-2003 shared task on NER as mentioned in this paper was the first NER task with language-independent named entity recognition (NER) data sets and evaluation method, and a general overview of the systems that participated in the task and their performance.
Journal ArticleDOI

The Proposition Bank: An Annotated Corpus of Semantic Roles

TL;DR: An automatic system for semantic role tagging trained on the corpus is described and the effect on its performance of various types of information is discussed, including a comparison of full syntactic parsing with a flat representation and the contribution of the empty trace categories of the treebank.
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