Complex noun phrases are pervasive in biomedical texts, but are largely under-explored in entity discovery and information extraction. Such expressions often contain a mix of highly specific names (diseases, drugs, etc.) and common words such as "condition", "degree", "process", etc. These words can have different semantic types depending on their context in noun phrases. In this paper, we address the task of classifying these common words onto fine-grained semantic types: for instance, "condition" can be typed as "symptom and finding" or "configuration and setting". For information extraction tasks, it is crucial to consider common nouns only when they really carry biomedical meaning; hence the classifier must also detect the negative case when nouns are merely used in a generic, uninforma-tive sense. Our solution harnesses a small number of labeled seeds and employs label propagation, a semisupervised learning method on graphs. Experiments on 50 frequent nouns show that our method computes semantic labels with a micro-averaged accuracy of 91.34%.
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