Shallow Semantics for Coreference Resolution
Proceedings of the Twentieth International Joint Conference on Artificial Intelligence. Pages 1689-1693.
Abstract
This paper focuses on the linguistic aspect of noun phrase coreference, investigating the knowledge sources that can potentially improve a learning-based coreference resolution system. Unlike traditional, knowledge-lean coreference resolvers, which rely almost exclusively on morpho-syntactic cues, we show how to induce features that encode semantic knowledge from labeled and unlabeled corpora. Experiments on the ACE data sets indicate that the addition of these new semantic features to a coreference system employing a fairly standard feature set significantly improves its performance.
@inproceedings{ijcai2007p273,
title = {Shallow Semantics for Coreference Resolution},
author = {Ng, Vincent},
booktitle = {Proceedings of the Twentieth International Joint Conference on
Artificial Intelligence, {IJCAI-07}},
publisher = {International Joint Conferences on Artificial Intelligence},
editor = {Ramon López de Mántaras},
pages = {1689--1693},
year = {2007},
url = {https://ijcai.org/proceedings/2007/273},
}
TY - CPAPER TI - Shallow Semantics for Coreference Resolution AU - Ng, Vincent T2 - Proceedings of the Twentieth International Joint Conference on Artificial Intelligence, IJCAI-07 PB - International Joint Conferences on Artificial Intelligence PY - 2007 SP - 1689 EP - 1693 UR - https://ijcai.org/proceedings/2007/273 ER -Copied
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