  This paper focuses on a traditional relation extraction task in the context
of limited annotated data and a narrow knowledge domain. We explore this task
with a clinical corpus consisting of 200 breast cancer follow-up treatment
letters in which 16 distinct types of relations are annotated. We experiment
with an approach to extracting typed relations called window-bounded
co-occurrence (WBC), which uses an adjustable context window around entity
mentions of a relevant type, and compare its performance with a more typical
intra-sentential co-occurrence baseline. We further introduce a new
bag-of-concepts (BoC) approach to feature engineering based on the
state-of-the-art word embeddings and word synonyms. We demonstrate the
competitiveness of BoC by comparing with methods of higher complexity, and
explore its effectiveness on this small dataset.
