Abstract
Alignment between concepts in an abstract meaning representation (AMR) graph and the words within a sentence is one of the important stages of AMR parsing. Although there exist high performing AMR aligners for English, unfortunately, these are not well suited for many languages where many concepts appear from morpho-semantic elements. For the first time in the literature, this paper presents an AMR aligner tailored for morphologically-rich and pro-drop languages by experimenting on the Turkish language being a prominent example of this language group. Our aligner focuses on the meaning considering the rich Turkish morphology and aligns AMR concepts that emerge from morphemes using a tree traversal approach without additional resources or rules. We evaluate our aligner over a manually annotated gold data set. Our aligner outperforms the Turkish adaptations of the previously proposed aligners for English and Portuguese by an F1 score of 0.87 and provides a relative error reduction of up to 76%.
Original language | English |
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Title of host publication | ACL 2022 - 60th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Student Research Workshop |
Editors | Samuel Louvan, Andrea Madotto, Brielen Madureira |
Publisher | Association for Computational Linguistics (ACL) |
Pages | 143-152 |
Number of pages | 10 |
ISBN (Electronic) | 9781955917230 |
Publication status | Published - 2022 |
Event | 60th Annual Meeting of the Association for Computational Linguistics, ACL 2022 - Dublin, Ireland Duration: 22 May 2022 → 27 May 2022 |
Publication series
Name | Proceedings of the Annual Meeting of the Association for Computational Linguistics |
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ISSN (Print) | 0736-587X |
Conference
Conference | 60th Annual Meeting of the Association for Computational Linguistics, ACL 2022 |
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Country/Territory | Ireland |
City | Dublin |
Period | 22/05/22 → 27/05/22 |
Bibliographical note
Publisher Copyright:© 2022 Association for Computational Linguistics.