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Found 6 papers, 3 papers with code
NL-Augmenter: A Framework for Task-Sensitive Natural Language Augmentation
Data augmentation is an important component in the robustness evaluation of models in natural language processing (NLP) and in enhancing the diversity of the data they are trained on.
Linguistically Informed Relation Extraction and Neural Architectures for Nested Named Entity Recognition in BioNLP-OST 2019
Our RE system ranked first in the SeeDev-binary Relation Extraction Task with F1-score of 0. 3738.
Data Augmentation for Low-Resource Named Entity Recognition Using Backtranslation
The state of art natural language processing systems relies on sizable training datasets to achieve high performance.
Neural Architectures for Fine-Grained Propaganda Detection in News
To address the tasks of sentence (SLC) and fragment level (FLC) propaganda detection, we explore different neural architectures (e. g., CNN, LSTM-CRF and BERT) and extract linguistic (e. g., part-of-speech, named entity, readability, sentiment, emotion, etc.
Neural Text Classification and Stacked Heterogeneous Embeddings for Named Entity Recognition in SMM4H 2021
Our text classification submissions (team:MIC-NLP) have achieved competitive performance with F1-score of $0. 46$ and $0. 90$ on ADE Classification (Task 1a) and Profession Classification (Task 7a) respectively.
Domain Adaptive Pretraining for Multilingual Acronym Extraction
This paper presents our findings from participating in the multilingual acronym extraction shared task SDU@AAAI-22.
