Learning Parse and Translation Decisions From Examples With Rich Context
<|reference_start|>Learning Parse and Translation Decisions From Examples With Rich Context: We present a knowledge and context-based system for parsing and translating natural language and evaluate it on sentences from the Wall Street Journal. Applying machine learning techniques, the system uses parse action examples acquired under supervision to generate a deterministic shift-reduce parser in the form of a decision structure. It relies heavily on context, as encoded in features which describe the morphological, syntactic, semantic and other aspects of a given parse state.<|reference_end|>
@article{hermjakob1997learning,
    title={Learning Parse and Translation Decisions From Examples With Rich Context},
    author={Ulf Hermjakob, Raymond J. Mooney (Dept. of Computer Sciences,
  University of Texas at Austin)},
    journal={Proceedings of ACL/EACL'97},
    year={1997},
    archivePrefix={arXiv},
    eprint={cmp-lg/9706002},
    primaryClass={cmp-lg cs.CL}
}