The TRAINS 93 Dialogues
Building a large annotated corpus of English: the penn treebank
Computational Linguistics - Special issue on using large corpora: II
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 1
Recovering implicit information
ACL '86 Proceedings of the 24th annual meeting on Association for Computational Linguistics
The Proposition Bank: An Annotated Corpus of Semantic Roles
Computational Linguistics
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Beyond NomBank: a study of implicit arguments for nominal predicates
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
SemEval-2010 task 10: Linking events and their participants in discourse
SemEval '10 Proceedings of the 5th International Workshop on Semantic Evaluation
SEMAFOR: Frame argument resolution with log-linear models
SemEval '10 Proceedings of the 5th International Workshop on Semantic Evaluation
VENSES++: Adapting a deep semantic processing system to the identification of null instantiations
SemEval '10 Proceedings of the 5th International Workshop on Semantic Evaluation
Desperately seeking implicit arguments in text
RELMS '11 Proceedings of the ACL 2011 Workshop on Relational Models of Semantics
Casting implicit role linking as an anaphora resolution task
SemEval '12 Proceedings of the First Joint Conference on Lexical and Computational Semantics - Volume 1: Proceedings of the main conference and the shared task, and Volume 2: Proceedings of the Sixth International Workshop on Semantic Evaluation
Exploiting Explicit Annotations and Semantic Types for Implicit Argument Resolution
ICSC '12 Proceedings of the 2012 IEEE Sixth International Conference on Semantic Computing
Annotating the argument structure of deverbal nominalizations in Spanish
Language Resources and Evaluation
Semantic role labeling of implicit arguments for nominal predicates
Computational Linguistics
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This paper deals with the automatic identification and annotation of the implicit arguments of deverbal nominalizations in Spanish. We present the first version of the LIAR system focusing on its classifier component. We have built a supervised Machine Learning feature based model that uses a subset of AnCora-Es as a training corpus. We have built four different models and the overall F-Measure is 89.9%, which means an increase F-Measure performance approximately 35 points over the baseline (55%). However, a detailed analysis of the feature performance is still needed. Future work will focus on using LIAR to automatically annotate the implicit arguments in the whole AnCora-Es.