Papers by Aleksander Leczkowski

2 papers
Comparing learnability of two dependency schemes: ‘semantic’ (UD) and ‘syntactic’ (SUD) (2021.findings-emnlp)

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Challenge: Several studies have suggested that choosing syntactic criteria for assigning heads in dependency trees improves the performance of dependency parsers.
Approach: They propose to use syntactic criteria to assign heads to dependency trees to improve the performance of dependency parsers by using a selection of 21 treebanks.
Outcome: The proposed approach favours content words over function words as heads of dependency relations, while the other favours syntactic heads.
Prepositions Matter in Quantifier Scope Disambiguation (2022.coling-1)

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Challenge: Existing work on how to integrate world knowledge into a QSD model has been limited .
Approach: They use a scope-disambiguated corpus annotated with prepositional senses to integrate our knowledge into a machine learning model.
Outcome: The proposed model is based on a scope-disambiguated corpus annotated with prepositional senses . Statistical analysis shows that prepositions have a positive impact on the learnability of automatic QSD systems.

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