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A Morpho-Syntactically Informed LSTM-CRF Model for Named Entity Recognition
We propose a morphologically informed model for named entity recognition, which is based on LSTM-CRF architecture and combines word embeddings, Bi-LSTM character embeddings, part-of-speech (POS) tags, and morphological information.
Gradient Emotional Analysis
The main focus of this proposal is to categorize a given sentence in two dimensions - sentiment and arousal.
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