Learning to Identify Reduced Passive Verb Phrases with a Shallow Parser
Abstract
Our research is motivated by the observation that NLP sys-tems frequently mislabel passive voice verb phrases as being in the active voice when there is no auxiliary verb (e.g., “The man arrested had a long record”). These errors directly im-pact thematic role recognition and NLP applications that de-pend on it. We present a learned classifier that can accurately identify reduced passive voice constructions in shallow pars-ing environments.
Cite
Text
Igo and Riloff. "Learning to Identify Reduced Passive Verb Phrases with a Shallow Parser." AAAI Conference on Artificial Intelligence, 2008.Markdown
[Igo and Riloff. "Learning to Identify Reduced Passive Verb Phrases with a Shallow Parser." AAAI Conference on Artificial Intelligence, 2008.](https://mlanthology.org/aaai/2008/igo2008aaai-learning/)BibTeX
@inproceedings{igo2008aaai-learning,
title = {{Learning to Identify Reduced Passive Verb Phrases with a Shallow Parser}},
author = {Igo, Sean and Riloff, Ellen},
booktitle = {AAAI Conference on Artificial Intelligence},
year = {2008},
pages = {1458-1461},
url = {https://mlanthology.org/aaai/2008/igo2008aaai-learning/}
}