Learning Transformation Rules to Find Grammatical Relations

نویسندگان

  • Lisa Ferro
  • Marc B. Vilain
  • Alexander S. Yeh
چکیده

Appears in Computational Natural Language Learning (CoNLL-99), pages 43-52. A workshop at the 9th Conf. of the European Chapter of the Assoc. for Computational Linguistics (EACL-99). Bergen, Norway, June, 1999. cs.CL/9906015 Grammatical relationships are an important level of natural language processing. We present a trainable approach to find these relationships through transformation sequences and error-driven learning. Our approach finds grammatical relationships between core syntax groups and bypasses much of the parsing phase. On our training and test set, our procedure achieves 63.6% recall and 77.3% precision (f-score = 69.8).

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تاریخ انتشار 1999