Integrating Text Embedding with Traditional NLP Features for Clinical Relation Extraction

Recently, text embedding techniques such as Word2Vec and BERT have produced state-of-the-art results in a wide variety of NLP tasks. As a result, traditional NLP features frequently used in Information Extraction (IE) such as POS tags, dependency relations and semantic types have received less attention. In this paper, we investigate whether traditional NLP features can be combined with word and sentence embeddings to improve relation extraction. We have explored diverse feature sets and different neural network architectures and evaluated our models on a benchmark clinical text dataset. Our new models significantly outperformed all the baselines on the same dataset.