POS Tagging for Twitter

TensorFlow implementation of Transferring from Formal Newswire Domain with Hypernet for Twitter POS Tagging.

The code is partially referred to https://github.com/guitaowufeng/TPANN and https://github.com/hardmaru/supercell/blob/master/supercell.py

Requirements

• Python 2.7 or higher
• Numpy
• Tensorflow 1.0
• Gensim

In addition, anyone who want to run these codes should download the word embedding from https://github.com/guitaowufeng/TPANN. The files 'word2vec_200dim.model... ' should be placed at 'dada/'.

Usage

1.Reproducing the results of paper:

$ python hyper_train.py

which will reproduce the results of the paper. The code will save the best model for future use.

2.retraining the model

You can change the hyperparameters to observe the change of results. Please run

$ python hyper_train.py --## ##

The parameters in this model are:

params	meaning	default
rnn_size	size of LSTM internal state	250
kernels	CNN kernel widths	[1,2,3,4,5,6]
kernel_features	number of features in the CNN kernel	[50,50,100,100,200,200]
hyper_input_size	window of context	7
char_embed_size	dimensionality of character embeddings	25
word_embed_size	dimensionality of word embeddings	200
max_word_length	maximum word length	35
param_init	initialize parameters at	0.05
batch_size	number of sequences to train on in parallel	20
max_epochs	number of full passes through the training data	100
and so on.

Reference

Transferring from Formal Newswire Domain with Hypernet for Twitter POS Tagging