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CoNLL-2012 Shared Task: Modeling Multilingual Unrestricted Coreference in OntoNotes
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. Expand
Towards Robust Linguistic Analysis using OntoNotes
An analysis of the performance of publicly available, state-of-the-art tools on all layers and languages in the OntoNotes v5.0 corpus should set the benchmark for future development of various NLP components in syntax and semantics, and possibly encourage research towards an integrated system that makes use of the various layers jointly to improve overall performance. Expand
CoNLL-2011 Shared Task: Modeling Unrestricted Coreference in OntoNotes
The CoNLL-2011 shared task involved predicting coreference using OntoNotes data, a new resource that provides multiple integrated annotation layers (parses, semantic roles, word senses, named entities and coreference) that could support joint models. Expand
SemEval-2010 Task 14: Word Sense Induction &Disambiguation
The description and evaluation framework of SemEval-2010 Word Sense Induction & Disambiguation task, as well as the evaluation results of 26 participating systems, are presented. Expand
SemEval-2007 Task-17: English Lexical Sample, SRL and All Words
This paper describes our experience in preparing the data and evaluating the results for three subtasks of SemEval-2007 Task-17 - Lexical Sample, Semantic Role Labeling (SRL) and All-WordsExpand
Support Vector Learning for Semantic Argument Classification
A machine learning algorithm for semantic role parsing is proposed, extending the work of Gildea and Jurafsky (2002), Surdeanu et al. (2003) and others, based on Support Vector Machines which shows large improvement in performance over earlier classifiers. Expand
Shallow Semantic Parsing using Support Vector Machines
A machine learning algorithm for shallow semantic parsing based on Support Vector Machines which shows performance improvements through a number of new features and their ability to generalize to a new test set drawn from the AQUAINT corpus. Expand
A Transition-based Algorithm for AMR Parsing
A two-stage framework to parse a sentence into its Abstract Meaning Representation (AMR) by using a dependency parser to generate a dependency tree and a novel transition-based algorithm that transforms the dependency tree to an AMR graph. Expand
Unrestricted Coreference: Identifying Entities and Events in OntoNotes
An initial model for unrestricted coreference based on data that uses a machine learning architecture with state-of-the-art features is presented and an analysis of the contribution of this new resource in the context of recent MUC and ACE results is provided. Expand
Semantic Role Labeling Using Different Syntactic Views
A state-of-the-art baseline semantic role labeling system based on Support Vector Machine classifiers is presented and improvements on this system are shown by adding new features including features extracted from dependency parses, performing feature selection and calibration and combining parses obtained from semantic parsers trained using different syntactic views. Expand