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About Evaluating The Utility of Hand-Crafted Features in Sequence Labelling
1. The document evaluates the utility of hand-crafted features in sequence labeling models by proposing a hybrid neural network model for named entity recognition (NER) that incorporates an auto-encoder loss to reconstruct hand-crafted features. 2. The model extends a previous Bi-LSTM+CNN+CRF model by taking hand-crafted features as both input and output to the auto-encoder loss component. 3. Evaluating on the CoNLL-2003 English NER task, the model that includes features for part-of-speech, word shapes, and gazetteers achieves a new state-of-the-art F1 score of 91.89, outperforming other competitive baselines
Detailed Information
| Author: | ['Rithvik Sharma'] |
|---|---|
| Publication Year: | 2019 |
| Pages: | 7 |
| Language: | English |
| Format: | |
| Price: | FREE |
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