DEEPER: A Full Parsing Based Approach to Protein Relation Extraction

Lexical variance in biomedical texts poses a challenge to automatic protein relation mining. We therefore propose a new approach that relies only on more general language structures such as parsing and dependency information for the construction of feature vectors that can be used by standard machine learning algorithms in deciding whether a sentence describes a protein interaction or not. As our approach is not dependent on the use of specific interaction keywords, it is applicable to heterogeneous corpora. Evaluation on benchmark datasets shows that our method is competitive with existing state-of-the-art algorithms for the extraction of protein interactions.

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