Papers by Jana Straková
OOVs in the Spotlight: How to Inflect Them? (2024.lrec-main)
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| Challenge: | Inflection is a process of word formation in which a base word form (lemma) is modified to express grammatical categories. |
| Approach: | They develop a retrograde model and two sequence-to-sequence models based on LSTM and Transformer. |
| Outcome: | The proposed systems outperform the existing systems on 9 out of 16 languages in the OOV evaluation. |
Neural Architectures for Nested NER through Linearization (P19-1)
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| Challenge: | a nested named entity recognition (NER) is a set of entities that can overlap and be labeled with more than one label. |
| Approach: | They propose two neural network architectures for nested named entity recognition . they propose to model nesting entities as multilabels and predict a sequence-to-sequence problem . |
| Outcome: | The proposed methods outperform the state-of-the-art on four corpora . the proposed models also improve on the recently published contextual embeddings . |
NameTag 3: A Tool and a Service for Multilingual/Multitagset NER (2025.acl-demo)
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| Challenge: | NameTag 3 is an open-source tool and cloud-based web service for named entity recognition. |
| Approach: | NameTag 3 is an open-source tool and cloud-based web service for named entity recognition. |
| Outcome: | NameTag 3 achieves state-of-the-art on 21 test datasets in 15 languages . available as command-line tool and as cloud-based service, enabling use without local installation . |
Czech Grammar Error Correction with a Large and Diverse Corpus (2022.tacl-1)
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| Challenge: | a large and diverse corpus of Czech grammar error correction corpora is available for other languages . despite efforts to mitigate the notorious shortage of national GEC-annotated corpors, the lack of adequate data is even more acute in languages other than English. |
| Approach: | They propose to annotate a large and diverse Czech corpus for grammar error correction . they compare several Czech GEC systems and meta-evaluate common GEC metrics against human judgments on data. |
| Outcome: | The proposed corpus is annotated for grammar error correction (GEC) in Czech. |