The description of events in biomedical literature often follows discourse

patterns. For example, authors may firstly mention the transcription of a

gene, and then go on to describe how this transcription is regulated by another

gene. Capturing such patterns can be beneficial when we want to extract event

mentions from literature. For instance, detecting the mention of a transcription of

gene A gives us a hint to actively look for mentions of regulations involving A.

With this hint we could find such mentions even if they follow unseen lexical or

syntactic patterns. To exploit such hints we need to perform event extraction in a

cross sentence manner.

It is shown that imperatively defined factor graphs (IDF) are an intuitive way to

build Markov Networks that model inter-dependencies between mentions of events

within sentences, and across sentence-boundaries. Small pieces of procedural code

define the graph structure, feature functions and hooks for efficient inference.

Empirically, this leads to an efficient cross-sentence event extractor with very

competitive results on the BioNLP shared task. One of our inter-event features

shows an impact of 1:94 points in F1 for the class of regulation events.