Papers by Ana Ezquerro
Dependency Graph Parsing as Sequence Labeling (2024.emnlp-main)
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| Challenge: | Various linearizations have been proposed to cast syntactic dependency parsing as sequence labeling, but they cannot handle reentrancy or cycles. |
| Approach: | They propose unbounded linearizations that can be used to cast dependency parsing as sequence labeling. |
| Outcome: | The proposed linearizations can cast syntactic dependency parsing as a sequence labeling task. |
Bringing Emerging Architectures to Sequence Labeling in NLP (2026.eacl-long)
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| Challenge: | Pretrained Transformer encoders are the dominant approach to sequence labeling . however, few have been applied to sequence labels on flat or simplified tasks . |
| Approach: | They propose to use pretrained Transformer encoders to model relations across words . they find that the architectures adapt well across tagging tasks that vary in complexity . |
| Outcome: | The proposed architectures perform well across tagging tasks across languages and datasets. |
From Partial to Strictly Incremental Constituent Parsing (2024.eacl-short)
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| Challenge: | Incremental NLP aims to learn and adapt partial representations as information unfolds, but studies on incremental approaches have focused on non-incremental approaches. |
| Approach: | They build incremental constituent parsers that adhere to a strong definition of incrementality across languages and compare them against non-incremental and partially incremental models. |
| Outcome: | The proposed models are compared against non-incremental and partially incremental models. |
Hierarchical Bracketing Encodings Work for Dependency Graphs (2025.emnlp-main)
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| Challenge: | Sequence labeling (SL) is a simple yet effective paradigm for a wide range of natural language problems. |
| Approach: | They propose a new bracketing approach for dependency graph parsing that encodes graphs as sequences and n tagging actions. |
| Outcome: | The proposed approach significantly reduces label space while preserving structural information. |
Hierarchical Bracketing Encodings for Dependency Parsing as Tagging (2025.acl-long)
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| Challenge: | Existing encodings for dependency parsing use suboptimal number of labels and a limited number of symbols. |
| Approach: | They propose a family of encodings for sequence labeling dependency parsing based on hierarchical bracketing . they propose an optimal hierarchically bracketing which minimizes the number of symbols used and encodes projective trees using only 12 distinct labels . |
| Outcome: | The proposed encodings yield competitive accuracy on a diverse set of treebanks. |