CARAMBA: Concept, Assertion, and Relation Annotation using Machine-learning Based Approaches

By Cyril Grouin, Asma Ben Abacha, Delphine Bernhard, Bruno Cartoni, Louise Deléger, Brigitte Grau, Anne-Laure Ligozat, Anne-Lyse Minard, Sophie Rosset, Pierre Zweigenbaum. 2010

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Recurrent Neural Networks for Classifying Relations in Clinical Notes (2017, neutral): “Other teams complemented their machine learning systems with rules that capture simple linguistic patterns of relations [18].”
Segment convolutional neural networks (Seg-CNNs) for classifying relations in clinical notes (2018, background): “Others supplemented their machine learning systems with rules that capture linguistic patterns of relations.(23,25,26) One of the top-performing teams(17) performed a follow-up study by employing a composite kernel–based model that consists of concept kernels, connection kernels, and tree kernels in”
2010 i2b2/VA challenge on concepts, assertions, and relations in clinical text (2011, methodology): “21 25 39 Anick et al ’ s system 39 used lists of n-grams with speci ﬁ c semantics, Divita et al 37 augmented the reference standard for the least prevalent relation classes, Demner-Fushman et al 35 utilized UMLS CUIs 43 and exercised feature reduction through cross-validation, and Grouin et al 36 co”