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Introduction to the CoNLL-2005 Shared Task: Semantic Role Labeling
TLDR
The specification and goals of the task are introduced, the data sets and evaluation methods are described, and a general overview of the 19 systems that have contributed to the task is presented, providing a comparative description and results. Expand
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. Expand
The CoNLL-2009 Shared Task: Syntactic and Semantic Dependencies in Multiple Languages
TLDR
This shared task combines the shared tasks of the previous five years under a unique dependency-based formalism similar to the 2008 task and describes how the data sets were created and show their quantitative properties. Expand
SVMTool: A general POS Tagger Generator Based on Support Vector Machines
TLDR
The SVMTool offers a fairly good balance among these properties which make it really practical for current NLP applications, and it is very easy to use and easily configurable so as to perfectly fit the needs of a number of different applications. Expand
SemEval-2010 Task 1: Coreference Resolution in Multiple Languages
TLDR
An insight is provided into (i) the portability of coreference resolution systems across languages, and (ii) the effect of different scoring metrics on ranking the output of the participant systems. Expand
Introduction to the CoNLL-2004 Shared Task: Semantic Role Labeling
TLDR
The specification and goal of the task are introduced, the data sets and evaluation methods are described, and a general overview of the systems that have contributed to the task is presented, providing comparative description. Expand
Boosting Trees for Anti-Spam Email Filtering
TLDR
The boosting-based methods clearly outperform the baseline learning algorithms on the PU1 corpus, achieving very high levels of the F1 measure and obtaining better ``high-precision'' classifiers, which is a very important issue when misclassification costs are considered. Expand
Special Issue Introduction: Semantic Role Labeling: An Introduction to the Special Issue
TLDR
This issue assesses weaknesses in semantic role labeling, identifies important challenges facing the field, and presents selected and representative work in the field. Expand
SemEval-2017 Task 3: Community Question Answering
TLDR
SemEval–2017 Task 3 on Community Question Answering reran the four subtasks from SemEval-2016, providing all the data from 2015 and 2016 for training, and fresh data for testing, and added a new subtask E in order to enable experimentation with Multi-domain Question Duplicate Detection in a larger-scale scenario, using StackExchange subforums. Expand
SemEval-2016 Task 3: Community Question Answering
TLDR
This paper describes the SemEval–2016 Task 3 on Community Question Answering, which was offered in English and Arabic, and found the best systems achieved an official score of 79.19, 76.70, 55.41, and 45.83 in subtasks A, B, C, and D. Expand
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