Using ALLiS for clausing

Authors:
Hervé Déjean
Affiliations:
Universität Tübingen
Venue:
ConLL '01 Proceedings of the 2001 workshop on Computational Natural Language Learning - Volume 7
Year:
2001

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Abstract

We present the result of a symbolic machine learning system, ALLiS 2.0 for the CoNLL-2001 shared task. ALLiS 2.0 is a theory refinement system using hierarchical data. Results are F=89.04 for subtask 1, F=68.02 for subtask 2 and F=67.70 for subtask 3 (development test). Adding manual rules improves considerably results specially for task 2 (F=79.44). For the test data, results are slightly worst (F=62.27 for subtask 3).