Effective Broad-Coverage Deep Parsing

James F. Allen, Omid Bahkshandeh, William de Beaumont, Lucian Galescu, Choh Man Teng

AAAI 2018 pp. 4776-4783

doi:10.1609/AAAI.V32I1.11934 /aaai/2018/allen2018aaai-effective/

Abstract

Current semantic parsers either compute shallow representations over a wide range of input, or deeper representations in very limited domains. We describe a system that provides broad-coverage, deep semantic parsing designed to work in any domain using a core domain-general lexicon, ontology and grammar. This paper discusses how this core system can be customized for a particularly challenging domain, namely reading research papers in biology. We evaluate these customizations with some ablation experiments

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Text

Allen et al. "Effective Broad-Coverage Deep Parsing." AAAI Conference on Artificial Intelligence, 2018. doi:10.1609/AAAI.V32I1.11934

Markdown

[Allen et al. "Effective Broad-Coverage Deep Parsing." AAAI Conference on Artificial Intelligence, 2018.](https://mlanthology.org/aaai/2018/allen2018aaai-effective/) doi:10.1609/AAAI.V32I1.11934

BibTeX

@inproceedings{allen2018aaai-effective,
  title     = {{Effective Broad-Coverage Deep Parsing}},
  author    = {Allen, James F. and Bahkshandeh, Omid and de Beaumont, William and Galescu, Lucian and Teng, Choh Man},
  booktitle = {AAAI Conference on Artificial Intelligence},
  year      = {2018},
  pages     = {4776-4783},
  doi       = {10.1609/AAAI.V32I1.11934},
  url       = {https://mlanthology.org/aaai/2018/allen2018aaai-effective/}
}