TY  - BOOK
AB  - 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.
DA  - 2011
LA  - eng
PY  - 2011
TI  - Inter-Event Dependencies support Event Extraction from Biomedical Literature
UR  - https://nbn-resolving.org/urn:nbn:de:0070-pub-26034724
Y2  - 2024-11-22T00:43:46
ER  -