Ryu Iida
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Abstract
We propose a machine learning-based approach to noun-phrase anaphora resolution that combines the advantages of previous learning-based models while overcoming their drawbacks. Our anaphora resolution process reverses the order of the steps in the classification-then-search model proposed by Ng and Cardie [2002b], inheriting all the advantages of that model. We conducted experiments on resolving noun-phrase anaphora in Japanese. The results show that with the selection-then-classification-based modifications, our proposed model outperforms earlier learning-based approaches.
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Index Terms
- Anaphora resolution by antecedent identification followed by anaphoricity determination
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