  Automatic understanding of domain specific texts in order to extract useful
relationships for later use is a non-trivial task. One such relationship would
be between railroad accidents' causes and their correspondent descriptions in
reports. From 2001 to 2016 rail accidents in the U.S. cost more than $4.6B.
Railroads involved in accidents are required to submit an accident report to
the Federal Railroad Administration (FRA). These reports contain a variety of
fixed field entries including primary cause of the accidents (a coded variable
with 389 values) as well as a narrative field which is a short text description
of the accident. Although these narratives provide more information than a
fixed field entry, the terminologies used in these reports are not easy to
understand by a non-expert reader. Therefore, providing an assisting method to
fill in the primary cause from such domain specific texts(narratives) would
help to label the accidents with more accuracy. Another important question for
transportation safety is whether the reported accident cause is consistent with
narrative description. To address these questions, we applied deep learning
methods together with powerful word embeddings such as Word2Vec and GloVe to
classify accident cause values for the primary cause field using the text in
the narratives. The results show that such approaches can both accurately
classify accident causes based on report narratives and find important
inconsistencies in accident reporting.
