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Cited by 257

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Exploiting semantic role labeling, WordNet and Wikipedia for coreference resolution

Open access
Ponzetto, Simone Paolo, Strube, Michael·2006·Proceedings of the main conference on Human Language Technology Conference of the North American Chapter of the Association of Computational Linguistics -

Abstract

In this paper we present an extension of a machine learning based coreference resolution system which uses features induced from different semantic knowledge sources. These features represent knowledge mined from WordNet and Wikipedia, as well as information about semantic role labels. We show that semantic features indeed improve the performance on different referring expression types such as pronouns and common nouns.

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Metadata

Authors
Ponzetto, S. P., & Strube, M.
Venue
Proceedings of the main conference on Human Language Technology Conference of the North American Chapter of the Association of Computational Linguistics -
Published
2006
Pages
192-199
OpenAlex
W2145679919
Cited by
257
References
26
FWCI
14.89
Type
conference paper
Language
en
Open access
gold

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CoChat. (2026). Citation check: Exploiting semantic role labeling, WordNet and Wikipedia for coreference resolution. https://app.cochat.ai/papers/10.3115/1220835.1220860
The paper
Ponzetto, S. P., & Strube, M. (2006). Exploiting semantic role labeling, WordNet and Wikipedia for coreference resolution. Proceedings of the main conference on Human Language Technology Conference of the North American Chapter of the Association of Computational Linguistics -. https://doi.org/10.3115/1220835.1220860

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.