A 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-CNNsPaper | Code | Results | Date | Stars |
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Task | Papers | Share |
---|---|---|
Named Entity Recognition | 6 | 16.67% |
Dependency Parsing | 2 | 5.56% |
Feature Engineering | 2 | 5.56% |
Person Re-Identification | 1 | 2.78% |
Earthquake prediction | 1 | 2.78% |
Time Series Forecasting | 1 | 2.78% |
Voice Query Recognition | 1 | 2.78% |
Sign Language Recognition | 1 | 2.78% |
Intent Classification | 1 | 2.78% |