An investigation of broad coverage automatic pronoun resolution for information retrieval
By Richard J. Edens, Helen Gaylard, Gareth J. F. Jones, Adenike M. Lam-Adesina. 2003
9 citations recorded by the source corpus.
Term weighting methods have been shown to give significant increases in information retrieval performance. The presence of pronomial references in documents reduces the term frequencies of associated words with a consequent effect on term weights and information retrieval behaviour. This investigation explores the impact on information retrieval performance of broad coverage automatic pronoun resolution. Results indicate that this approach has potential to improve both precision at fixed cutoff levels and average precision.
Subfields: Artificial Intelligence, Information Systems
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In Artificial Intelligence
- Yang Trista Cao
- Hal Daumé Iii
- Trevor Darrell
- Kate Saenko
- Qiang Yang
- Mohammad Rostami
- Ramalingam Chellappa
- Mohit Bansal
In Information Systems
Citations in the corpus
- Toward Gender-Inclusive Coreference Resolution (2019, neutral): “For instance, information retrieval can use coreference resolution to help accurately rank documents by up-weighting the importance of entities that are referred to frequently [Du and Liddy 1990; Edens et al. 2003; Pirkola and Järvelin 1996].”
- Toward Gender-Inclusive Coreference Resolution: An Analysis of Gender and Bias Throughout the Machine Learning Lifecycle* (2021, background): “For instance, information retrieval can use coreference resolution to help accurately rank documents by up-weighting the importance of entities that are referred to frequently (Du and Liddy 1990; Pirkola and Järvelin 1996; Edens et al. 2003).”