Scientific Discourse Tagger (SciDT)

LSTM based sequence labeling model for scientific discourse tagger. Read the paper for more details.

Requirements

• Theano (tested with v0.8.0)
• Keras (tested with v0.3.2)
• Pretrained word embedding (recommended: http://bio.nlplab.org/#word-vectors): SciDT expects a gzipped embedding file with each line containing word and a the vector (list of floats) separated by spaces

Input Format

SciDT expects inputs to lists of clauses, with paragraph boundaries identified, i.e., each line in the input file needs to be a clause and and paragraphs should be separated by blank lines.

If you are training, the file additionally needs labels at the clause level, which can be specified on each line, after the clause, separated by a tab. Please look at the sample train and test files for the expected format.

Intended Usage

As mentioned in the paper, the model is intended for tagging discourse elements in experiment narratives in biomedical research papers, and we use the seven label taxonomy described in De Waard and Pander Maat (2012). The taxonomy is defined at the clause level, which is why we assume that each line in the input file is a clause. However, the model itself is more general than this, and can be put to use for tagging other kinds of discourse elements as well, even at the sentence level. If you find other uses for this code, I would love to hear about it!

Training

python nn_passage_tagger.py --repfile REPFILE --train_file TRAINFILE --use_attention

where REPFILE is the embedding file. --use_attention is recommended. Check out the help messages for nn_passage_tagger.py for more options

Trained model

After you train successfully, three new files appear in the directory, with file names containing chosen values for att, cont and bi:

• model_att=*_cont=*_bi=*_config.json: The model description
• model_att=*_cont=*_bi=*_label_ind.json: The label index
• model_att=*_cont=*_bi=*_weights: Learned model weights

Testing

You can specify test files while training itself using --test_files arguments. Alternatively, you can do it after training is done. In the latter case, nn_passage_tagger assumes the trained model files described above are present in the directory.

python nn_passage_tagger.py REPFILE --test_files TESTFILE1 [TESTFILE2 ..] --use_attention

Make sure you use the same options for attention, context and bidirectional as you used for training.