Code for the paper: Transferring from Formal Newswire Domain with Hypernet for Twitter POS Tagging
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README.md

POS Tagging for Twitter

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

compgraph

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