We describe the task of automatically detecting interactions between proteins in biomedical literature. We use a syntactic parser, a corpus annotated for proteins, and manual decisions as training material.
After automatically parsing the GENIA corpus, which is manually annotated for proteins, all syntactic paths between proteins are extracted. These syntactic paths are manually disambiguated between meaningful paths and irrelevant paths. Meaningful paths are paths that express an interaction between the syntactically connected proteins, irrelevant paths are paths that do not convey any interaction. The resource created by these manual decisions is used in two ways. First, words that appear frequently inside a meaningful paths are learnt using simple machine learning. Second, these resources are applied to the task of automatically detecting interactions between proteins in biomedical literature. We use the IntAct corpus as an application corpus.
After detecting proteins in the IntAct texts, we automatically parse them and classify the syntactic paths between them using the meaningful paths from the resource created on GENIA and addressing sparse data problems by shortening the paths based on the words frequently appearing inside the meaningful paths, so-called transparent words.
We conduct an evaluation showing that we achieve acceptable recall and good precision, and we discuss the importance of transparent words for the task.