Incorporating Dictionaries into a Neural Network Architecture to Extract
  COVID-19 Medical Concepts From Social Media
Abul Hasan Mark Levene David Weston
We investigate the potential benefit of incorporating dictionary information
into a neural network architecture for natural language processing. In
particular, we make use of this architecture to extract several concepts
related to COVID-19 from an on-line medical forum. We use a sample from the
forum to manually curate one dictionary for each concept. In addition, we use
MetaMap, which is a tool for extracting biomedical concepts, to identify a
small number of semantic concepts. For a supervised concept extraction task on
the forum data, our best model achieved a macro $F_1$ score of 90\%. A major
difficulty in medical concept extraction is obtaining labelled data from which
to build supervised models. We investigate the utility of our models to
transfer to data derived from a different source in two ways. First for
producing labels via weak learning and second to perform concept extraction.
The dataset we use in this case comprises COVID-19 related tweets and we
achieve an $F_1$ score 81\% for symptom concept extraction trained on weakly
labelled data. The utility of our dictionaries is compared with a COVID-19
symptom dictionary that was constructed directly from Twitter. Further
experiments that incorporate BERT and a COVID-19 version of BERTweet
demonstrate that the dictionaries provide a commensurate result. Our results
show that incorporating small domain dictionaries to deep learning models can
improve concept extraction tasks. Moreover, models built using dictionaries
generalize well and are transferable to different datasets on a similar task.