Bidirectional Recurrent Neural Networks
CNN Bidirectional LSTM
Introduced by Chiu et al. in Named Entity Recognition with Bidirectional LSTM-CNNsA CNN BiLSTM is a hybrid bidirectional LSTM and CNN architecture. In the original formulation applied to named entity recognition, it learns both character-level and word-level features. The CNN component is used to induce the character-level features. For each word the model employs a convolution and a max pooling layer to extract a new feature vector from the per-character feature vectors such as character embeddings and (optionally) character type.
Source: Named Entity Recognition with Bidirectional LSTM-CNNs
Papers
| Paper | Code | Results | Date | Stars |
|---|
Tasks
| Task | Papers | Share |
|---|---|---|
| Named Entity Recognition | 5 | 17.86% |
| Dependency Parsing | 2 | 7.14% |
| Feature Engineering | 2 | 7.14% |
| Sign Language Recognition | 1 | 3.57% |
| Intent Classification | 1 | 3.57% |
| Knowledge Distillation | 1 | 3.57% |
| Natural Language Understanding | 1 | 3.57% |
| Clinical Assertion Status Detection | 1 | 3.57% |
| Clinical Concept Extraction | 1 | 3.57% |
BiLSTM
Convolution