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MedFilter

Improving Extraction of Task-relevant Utterances through Integration of Discourse Structure and Ontological Knowledge

Overview of MedFilter ...

Overview of MedFilter. MedFilter first encodes each utterance of the given conversation using a BERT-based encoder (A). The obtained utterance embedding is concatenated with contextual information like speaker role, position of utterance in the conversation, and ontological knowledge (B). This is then fed to a MS-BiLSTM (C1) for medical relevance identification. MS-BiLSTM leverages speaker role information to learn speaker-specific context for each utterance. This contextual representation is concatenated with the utterance embedding (C2) and passed through another MS-BiLSTM (C3) which focuses on fine-grained categorization. Both tasks are jointly learned. Refer to Section 3 for more details.

Code structure:

Install Dependencies

nltk==3.4.5, tqdm==4.41.1, sklearn==0.0, pymongo==3.10.1, pytorch==1.1.0, transformers(hugging-face)==2.3.0

Recover BERT-FT embeddings

Run bert_main.py to fine-tune the BERT embedding (bert-base-uncased) on the training set.

python bert_main.py --model bert --gpu <GPU> --seed 1 --name <NAME>

Convert the PyTorch model into TF model using HuggingFace transformers library.

python <TRANSFORMER_DIR>/transformers/src/transformers/convert_bert_pytorch_checkpoint_to_original_tf.py \
--model_name bert_model \
--pytorch_model_path ./bert_models/<NAME>/pytorch.bin \
--tf_cache_dir ./bert_models/<NAME>/tf_model

Put config and vocab files from original BERT model into the new directory.

cd ./bert_models/<NAME>/tf_model/
cp <bert_base_uncased_dir>/vocab.txt .
cp <bert_base_uncased_dir>/bert_config.json .
mv bert_base_uncased.ckpt.index bert_model.ckpt.index
mv bert_base_uncased.ckpt.meta bert_model.ckpt.meta
mv bert_base_uncased.ckpt.data-00000-of-00001 bert_model.ckpt.data-00000-of-00001
cd ..

Use bert-as-service to get the BERT representation for each utterance. Start the bert-as-service server on command line.

bert-serving-start -model_dir ./bert_models/<NAME>/tf_model/ -num_worker=2 -device_map 3 -max_seq_len 64

Then run dump_bert_client.py to dump the embedding for each conversation separately.

python dump_bert_client.py --embed <EMBED_NAME> --data_dir <DATA_DIR>

Once all embeddings are dumped to the directory (BERT_FT_EMBED_DIR), they are used as non-learnable features for MedTopic.

Recover UMLS Semantic Types

  1. Obtain a UMLS installation and then install QuickUMLS. This page provides detailed instructions to install both packages.
  2. QuickUMLS can then be used to map phrases in the text to CUIs and corresponding semantic-types (full list).
from quickumls import QuickUMLS
matcher = QuickUMLS(quickumls_fp, overlapping_criteria, threshold,
                    similarity_name, window, accepted_semtypes)

text = "You have diarrhoea"
matcher.match(text, best_match=True, ignore_syntax=False)

Output:

[[{'start': 9,
   'end': 18,
   'ngram': 'diarrhoea',
   'term': 'diarrhoea',
   'cui': 'C0011991',
   'similarity': 1.0,
   'semtypes': {'T184'},
   'preferred': 0}]]

Details about different arguments are provided here. We use default arguments for all QuickUMLS experiments in the paper.

Each utterance

Run utterance classifier

python multi_label_seq_tagger.py --batch 16 --data_split --embed <BERT_FT_EMBED_DIR> \
--epoch 300 --feat speaker,position,semantic --feat_cat 3,4,133 \
--feat_dim 8,4,8 --ind_cat all --l2 0.0 --log_db <LOG_DB_DIR> \
--loss med_tag,med_class --loss_fact 1.0,1.0 --lr 0.0005 --model multih-bilstm \
--name <NAME> --rnn_dim 1024 --rnn_drop 0.0 --rnn_layers 1 --seed 2 \
--target med_class_auc --gpu 0 --max_conv_len 128

Citation:

@inproceedings{khosla-etal-2020-medfilter,
    title = "{M}ed{F}ilter: {I}mproving {E}xtraction of {T}ask-relevant {U}tterances through {I}ntegration of {D}iscourse {S}tructure and {O}ntological {K}nowledge",
    author = "Khosla, Sopan  and
      Vashishth, Shikhar  and
      Lehman, Jill Fain  and
      Rose, Carolyn",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.626",
    doi = "10.18653/v1/2020.emnlp-main.626",
    pages = "7781--7797"
}

For any clarification, comments, or suggestions please create an issue or contact Sopan.