Bidirectional LSTM/CRF Training Algorithm

From GM-RKB
Jump to navigation Jump to search

A Bidirectional LSTM/CRF Training Algorithm is a supervised sequence segmentation algorithm that implements a bi-directional LSTM training algorithm and a CRF training algorithm.



References

2017d

1707.06799 Fig1.png 1707.06799 Tab12.png
Figure 1: Architecture of the BiLSTM network with a CRF-classifier. A fixed sized character-based representation is derived either with a Convolutional Neural Network or with a BiLSTM network. Table 12: Network configurations were sampled randomly and each was evaluated with each classifier as a last layer. The first number in a cell depicts in how many cases each classifier produced better results than the others. The second number shows the median difference to the best option for each task. Statistically significant differences with p < 0.01 are marked with †

2016a

2016b

CNN-arXiv-160301354.png BLSTM-CRF-arXiv-160301354.png
Figure 1: The convolution neural network for extracting character-level representations of words. Dashed arrows indicate a dropout layer applied before character embeddings are input to CNN. Figure 3: The main architecture of our neural network. The character representation for each word is computed by the CNN in Figure 1. Then the character representation vector is concatenated with the word embedding before feeding into the BLSTM network. Dashed arrows indicate dropout layers applied on both the input and output vectors of BLSTM.

2015