  Attorneys and others have a strong interest in having a digital library with
suitable services (e.g., summarizing, searching, and browsing) to help them
work with large corpora of legal depositions. Their needs often involve
understanding the semantics of such documents. That depends in part on the role
of the deponent, e.g., plaintiff, defendant, law enforcement personnel, expert,
etc. In the case of tort litigation associated with property and casualty
insurance claims, such as relating to an injury, it is important to know not
only about liability, but also about events, accidents, physical conditions,
and treatments.
  We hypothesize that a legal deposition consists of various aspects that are
discussed as part of the deponent testimony. Accordingly, we developed an
ontology of aspects in a legal deposition for accident and injury cases. Using
that, we have developed a classifier that can identify portions of text for
each of the aspects of interest. Doing so was complicated by the peculiarities
of this genre, e.g., that deposition transcripts generally consist of data in
the form of question-answer (QA) pairs. Accordingly, our automated system
starts with pre-processing, and then transforms the QA pairs into a canonical
form made up of declarative sentences. Classifying the declarative sentences
that are generated, according to the aspect, can then help with downstream
tasks such as summarization, segmentation, question-answering, and information
retrieval.
  Our methods have achieved a classification F1 score of 0.83. Having the
aspects classified with a good accuracy will help in choosing QA pairs that can
be used as candidate summary sentences, and to generate an informative summary
for legal professionals or insurance claim agents. Our methodology could be
extended to legal depositions of other kinds, and to aid services like
searching.
