Neural Token Representations and Negation and Speculation Scope Detection in Biomedical and General Domain Text

Codes for paper 'Neural Token Representations and Negation and Speculation Scope Detection in Biomedical and General Domain Text' by Elena Sergeeva, Henghui Zhu, Amir Tahmasebi and Peter Szolovits in LOUHI 2019

Environment

The script is running in Python 3.6. The requirements for the package is listed in the requirements.txt file.

Data

This repo contains the scripts for BioScope dataset. One can download the Biology Abstracts in the link and request the clinical report. By default, I put the xml file in folder data/raw.

I find there is some problems when I try to parse the document, which are related to the white space. Therefore, I provide the patch files for the xml. So one can run the following command to avoid those issue

patch data/raw/abstracts_pmid.xml -i data/raw/abstracts_pmid_modified.patch -o data/raw/abstracts_pmid_modified.xml
patch data/raw/clinical.xml -i data/raw/clinical_modified.patch -o data/raw/clinical_modified.xml

Preprocessing

In the preprocessing step, we need to first convert the xml files to json to be parsed easily in the next step.

python preprocessing/bioscope2json.py

Then we convert the json file into a standard conll format using

python preprocessing/json2conll_gold_cue.py

Finally, for bidirectional LSTM with Glove, ELMo and BERT models and BERT finetuning models, one can run conll2tfrecord_glove_gold_cue.py, conll2tfrecord_elmo_gold_cue.py, conll2tfrecord_bert_gold_cue.py, and conll2tfrecord_bert_ft_gold_cue.py in the preprocessing folder for generating the tfrecords files.

Model Training and Evaluation

For bidirectional LSTM with Glove, ELMo and BERT models and BERT finetuning model, one can run bilstm_glove_gold_cue.py, bilstm_elmo_gold_cue.py, bilstm_bert_gold_cue.py, and bert_finetune_gold_cue.py with flag --training=True for training. For evaluation, run the same script with flag --training=False .