Papers by Sharmistha Jat
Zero-shot Word Sense Disambiguation using Sense Definition Embeddings (P19-1)
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| Challenge: | Word Sense Disambiguation (WSD) is an open problem in Natural Language Processing . current methods treat senses as discrete labels and predict the most-frequent-Sense for unseen senses . |
| Approach: | They propose a supervised model to perform Word Sense Disambiguation (WSD) by predicting over a continuous sense embedding space rather than a discrete label space. |
| Outcome: | The proposed model generalizes over seen and unseen senses, achieving zero-shot learning. |
Relating Simple Sentence Representations in Deep Neural Networks and the Brain (P19-1)
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| Challenge: | Existing deep learning models for natural language processing are not fully studied. |
| Approach: | They investigate whether deep recurrent models learn sentences against those encoded by the brain and whether there is any correspondence between hidden layers of these models and brain regions when processing sentences. |
| Outcome: | The proposed models can be used to synthesize brain data and improve subsequent stimuli decoding accuracy. |