  The recognition and normalization of clinical information, such as tumor
morphology mentions, is an important, but complex process consisting of
multiple subtasks. In this paper, we describe our system for the CANTEMIST
shared task, which is able to extract, normalize and rank ICD codes from
Spanish electronic health records using neural sequence labeling and parsing
approaches with context-aware embeddings. Our best system achieves 85.3 F1,
76.7 F1, and 77.0 MAP for the three tasks, respectively.
