Hybrid Methods for Coreference Resolution in Swedish
Abstract: The aim of this thesis is to improve coreference resolution in Swedish by providing a hybrid approach based on combining data-driven methods and linguistic knowledge. Coreference resolution here consists in identifying all expressions in a text that have the same referent, for example, a person or an object.The linguistic knowledge is based on Accessibility Theory (Ariel 1990). This is used for guiding the selection of likely anaphor-antecedent pairs from the set of all possible such pairs in a text. The data-driven method adopted is Memory-Based Learning (MBL), a supervised method based on the idea that learning means storing experiences in memory, and that new problems are solved by reusing solutions from similar experiences (Daelemans and Van den Bosch 2005).The referring expressions covered by the system are names, definite descriptions, and pronouns. In order to maximize performance, we use different classifiers with a specific set of linguistically motivated features for each type of expression. The great majority of features used for classification are domain- and language-independent.We demonstrate two ways of using this method of linguistically motivated selection of anaphor-antecedent pairs.First, the amount of training examples stored in memory is reduced. We find that for coreference resolution of definite descriptions and names, the amount of training data can thereby be reduced with only a minor loss in performance, but for pronoun resolution there is a negative effect.Second, selection can be used for improving on coreference resolution results. This is the first step in our hybrid approach to coreference resolution, where the second step is the application of an MBL classifier for determining coreference between the selected pairs. Results indicate that this hybrid approach is advantageous for coreference resolution of definite descriptions and names. For pronoun resolution, there is a negative effect on recall along with a positive effect on precision.
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