An NLP model to classify abstract sentences into the role they play (e.g. objective, methods, results, etc..) to enable researchers to skim through the literature and dive deeper when necessary.

• More specificially, I'am going to replicate the deep learning model behind the 2017 paper PubMed 200k RCT: a Dataset for Sequenctial Sentence Classification in Medical Abstracts.

PubMed 200k RCT dataset

• The PubMed 200k RCT dataset is described in Franck Dernoncourt, Ji Young Lee. PubMed 200k RCT: a Dataset for Sequential Sentence Classification in Medical Abstracts. International Joint Conference on Natural Language Processing (IJCNLP). 2017.

Some miscellaneous information:

• PubMed 20k is a subset of PubMed 200k. I.e., any abstract present in PubMed 20k is also present in PubMed 200k.

• PubMed_200k_RCT is the same as PubMed_200k_RCT_numbers_replaced_with_at_sign, except that in the latter all numbers had been replaced by @. (same for PubMed_20k_RCT vs. PubMed_20k_RCT_numbers_replaced_with_at_sign).

• Count Plot

All the note books are availabel here

• NaiveBiase Model -> 72% Accuracy
• Conv1D Model -> 78% Accuracy
• Model using pretrained token embedding ( Universal sentence embedding ) -> 75% Accuracy
• Conv1D Model using character level embedding -> 73% Accuracy
• Model with both token and charcter level embedding -> 76% Accuracy
• Model with token, character and position level embedding ( https://arxiv.org/pdf/1612.05251.pdf ) -> 81% Accuracy

• Model described in this paper with bert embedding -> 88% Accuracy

• Tensorflow
• tensorflow_text
• tensorflow_hub
• sklearn
• Matplotlib
• numpy
• pandas
• spaCy