meshiguge / MDSENT

MDSENT: A Supervised System for Text Classification

An implementation of the MDSENT architectures described in the paper MDSENT at SemEval-2016 Task 4: A Supervised System for Message Polarity Classification by Hang Gao and Tim Oates.

Requirements

• Theano
• Numpy
• Lasagne
• Python 2.7

Usage

First run the following script

./fetch_and_preprocess.sh

This downloads the following data:

• TREC dataset (question classification task)
• Glove word vectors (Common Clawl 840B) -- PS: the download takes around 2GB

Alternatively, the download and preprocessing scripts can be called individually.

Training

This implementation only supports classification task. To train a network, run,

python2.7 train.py --train data/trec/train --dev data/trec/dev --batch 15 --epoch 10 --save model.pkg --word_vectors data/glove/glove.840B.300d.txt --word_vocab data/trec/wvocab-cased.txt --char_vocab data/trec/cvocab-cased.txt --w_filter 3 100 --w_filter 4 100 --w_filter 5 100 --c_filter 3 100 --c_filter 4 100 --c_filter 5 100 --num_class 6

where:

• train: the location of preprocessed training data
• dev: the location of preprocessed development data
• batch: the batch size
• epoch: number of training epochs
• save: the location to save trained model
• word_vectors: the location of pre-trained word embeddings
• char_vectors: the location of pre-trained character embeddings
• word_vocab: the location of preprocessed vocabulary for words
• char_vocab: the location of preprocessed vocabulary for characters
• w_filter: specifies a type of convolutional filter for word-based input with its height(int) and number(int)
• c_filter: specifies a type of convolutional filter for character-based input with its height(int) and number(int)
• num_class: number of classes for the classification task

Testing

To make predictions with a trained model, run,

python2.7 test.py --test data/trec/test --model model.pkg --word_vocab data/trec/wvocab-cased.txt --char_vocab data/trec/cvocab-cased.txt

where:

• test: the location of preprocessed test data
• model: the location of trained model
• word_vocab: the location of preprocessed vocabulary for words
• char_vocab: the location of preprocessed vocabulary for characters

PS: the implementation is in Theano, thus it is recommended that floatX is set to float32 in theano flags to avoid possible precision problems.