Papers with Part-of-Speech tagging
ZAEBUC: An Annotated Arabic-English Bilingual Writer Corpus (2022.lrec-1)
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| Challenge: | ZAEBUC is an annotated Arabic-English bilingual writer corpus . it is a corpus of short essays written by first-year university students . |
| Approach: | They propose to use a standard Arabic-English bilingual writer corpus to match comparable texts written by the same writer on different occasions. |
| Outcome: | The ZAEBUC corpus is an annotated Arabic-English bilingual writer corpus by first-year university students at Zayed University in the United Arab Emirates. |
How Bad are PoS Tagger in Cross-Corpora Settings? Evaluating Annotation Divergence in the UD Project. (N19-1)
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| Challenge: | Using annotation variation principles, Part-of-Speech tagging performance degrades when applied to test sentences that depart from training data. |
| Approach: | They propose to use the annotation variation principle to identify inconsistencies between annotations . they also evaluate their impact on prediction performance . |
| Outcome: | The proposed method can detect errors in gold standard annotations and improve prediction performance. |
Influence Functions for Sequence Tagging Models (2022.findings-emnlp)
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| Challenge: | Named Entity Recognition, Part-of-Speech tagging, and Semantic Role Labeling are standard tasks in NLP, but there has been little work on interpretability methods for sequence taging. |
| Approach: | They propose to extend influence functions to sequence tagging tasks by identifying noisy annotations in NER corpora. |
| Outcome: | The proposed methods are able to identify noisy annotations in NER corpora and are scalable. |
Pre-training and Evaluating Transformer-based Language Models for Icelandic (2022.lrec-1)
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| Challenge: | Pre-trained models obtain state-of-the-art performance on a wide variety of NLP tasks, including Question Answering (QA), Named Entity Recognition (NER), Part-of Speech (POS) tagging and Automatic Text Summarization (ATS). |
| Approach: | They pre-train four types of monolingual ELECTRA and ConvBERT models and compare them to a previously trained monolingual RoBERTa model and multilingual mBERT model. |
| Outcome: | The models outperform a multilingual model on four downstream tasks. |