"Bidirectional LSTM-CRF Models for Sequence Tagging" arXiv preprint arXiv:1508.01991 (2015). The POS-tag of a word is a label of the word indicating its part of speech as well as grammatical categories such as tense. text 'Senator Elizabeth Warren from Massachusetts announced her support of Social Security in Washington, D.C. I looked up some resources from online and found that the following code works. My implementation is based on the following paper: 1 I am a newbie in Python and would like to do POS tagging after importing csv file from my local machine. Eventually, i set batch size to 256 and it reached the highest accuarcy (at word level): 98.93%. Resultsįirst, i set batch size to 64, the model was overfitting at epoch 2, then i changed batch size to 128, it was at epoch 3. PTB POSįor Word Representation, i used pretrained word embedding Glove which each word corresponds to a 100-dimentional embedding vector. In corpus linguistics, part-of-speech tagging (POS tagging or PoS tagging or POST), also called grammatical tagging or word-category disambiguation. I test BI-LSTM-CRF networks on the Penn Treebank (POS tagging task), the table below shows the size of sentences, tokens and labels for training, validation and test sets respectively. max length (the number of timesteps): 141.Experimental results on the POS tagging corpus Penn Treebank (approximately 1 million tokens for Wall Street Journal) show that my model might achieve SOTA (reaching 98.93% accuracy at word level). My work is not the first to apply a BI-LSTM-CRF model to NLP sequence tagging benchmark datasets but it might achieve State-Of-The-Art (or nearly) results on POS tagging, even with NER. Bidirectional LSTM-CRF model for Sequence TaggingĪ Tensorflow 2/Keras implementation of POS tagging task using Bidirectional Long Short Term Memory (denoted as BiLSTM) with Conditional Random Field on top of that BiLSTM layer (at the inference layer) to predict the most relevant POS tags.
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