A non-projective dependency parser
ANLC '97 Proceedings of the fifth conference on Applied natural language processing
Identifying anaphoric and non-anaphoric noun phrases to improve coreference resolution
COLING '02 Proceedings of the 19th international conference on Computational linguistics - Volume 1
A computational approach to zero-pronouns in Spanish
ACL '00 Proceedings of the 38th Annual Meeting on Association for Computational Linguistics
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Inter-coder agreement for computational linguistics
Computational Linguistics
FeatureEng '05 Proceedings of the ACL Workshop on Feature Engineering for Machine Learning in Natural Language Processing
A Deeper Look into Features for Coreference Resolution
DAARC '09 Proceedings of the 7th Discourse Anaphora and Anaphor Resolution Colloquium on Anaphora Processing and Applications
AnCora-CO: Coreferentially annotated corpora for Spanish and Catalan
Language Resources and Evaluation
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In pro-drop languages, the detection of explicit subjects, zero subjects and non-referential impersonal constructions is crucial for anaphora and co-reference resolution. While the identification of explicit and zero subjects has attracted the attention of researchers in the past, the automatic identification of impersonal constructions in Spanish has not been addressed yet and this work is the first such study. In this paper we present a corpus to underpin research on the automatic detection of these linguistic phenomena in Spanish and a novel machine learning-based methodology for their computational treatment. This study also provides an analysis of the features, discusses performance across two different genres and offers error analysis. The evaluation results show that our system performs better in detecting explicit subjects than alternative systems.