Papers by Sara Meftah
A Neural Network Model for Part-Of-Speech Tagging of Social Media Texts (L18-1)
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| Challenge: | Recent approaches based on end-to-end Deep Neural Networks (DNNs) have shown promising results for Natural Language Processing (NLP). |
| Approach: | They propose a neural network model for part-of-speech (POS) tagging of User-Generated Content (UGC) such as Twitter, Facebook and Web forums that uses character and word representations. |
| Outcome: | The proposed model is end-to-end and uses character and word representations . it is compared with existing models on social media in English, german, french, italian and spanish . |
Joint Learning of Pre-Trained and Random Units for Domain Adaptation in Part-of-Speech Tagging (N19-1)
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| Challenge: | Pre-trained neural networks struggle with learning uncommon target-specific patterns. |
| Approach: | They propose to augment the target-network with normalised, weighted and randomly initialised units that beget a better adaptation while maintaining valuable source knowledge. |
| Outcome: | The proposed method achieves state-of-the-art on 3 commonly used datasets. |