2018
EMNLP
EMNLP 2018
Language Identification in Code-Mixed Data using Multichannel Neural Networks and Context Capture
Abstract
AbstractAn accurate language identification tool is an absolute necessity for building complex NLP systems to be used on code-mixed data. Lot of work has been recently done on the same, but there’s still room for improvement. Inspired from the recent advancements in neural network architectures for computer vision tasks, we have implemented multichannel neural networks combining CNN and LSTM for word level language identification of code-mixed data. Combining this with a Bi-LSTM-CRF context capture module, accuracies of 93.28% and 93.32% is achieved on our two testing sets.
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Interdisciplinary Bridge
— Artificial Intelligence and Deep Learning and Machine Learning and Natural Language Processing
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Keyword Pioneer
— multichannel neural network
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Hot Topic Early Bird
— code-mixed text
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Cross-Pollinator
— Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy, Speech & Audio
Authors
Topics
Machine Learning > Core Methods > Classification
Deep Learning > Architectures > Transformers
Deep Learning > Architectures > Neural Networks
Deep Learning > Techniques > Model Architecture
Natural Language Processing > Applications > Named Entity Recognition
Deep Learning > Learning Types > Multi-Modal Learning
Deep Learning > Learning Types > Classification
Artificial Intelligence > Core AI > Natural Language Processing
Deep Learning > Architectures > Recurrent Neural Networks