Search Results for author:
Found 8 papers, 2 papers with code
Domain Adaptive Pretraining for Multilingual Acronym Extraction
This paper presents our findings from participating in the multilingual acronym extraction shared task SDU@AAAI-22.
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.
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 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.
Content-based Recommendations for Radio Stations with Deep Learned Audio Fingerprints
That is why finding stations playing the preferred content is a tough task for a potential listener, especially due to the overwhelming number of offered choices.
Difficulty Classification of Mountainbike Downhill Trails utilizing Deep Neural Networks
The difficulty of mountainbike downhill trails is a subjective perception.
Soccer Team Vectors
In this work we present STEVE - Soccer TEam VEctors, a principled approach for learning real valued vectors for soccer teams where similar teams are close to each other in the resulting vector space.
Deep Neural Baselines for Computational Paralinguistics
Detecting sleepiness from spoken language is an ambitious task, which is addressed by the Interspeech 2019 Computational Paralinguistics Challenge (ComParE).
