  A law practitioner has to go through a lot of long legal case proceedings. To
understand the motivation behind the actions of different parties/individuals
in a legal case, it is essential that the parts of the document that express an
intent corresponding to the case be clearly understood. In this paper, we
introduce a dataset of 93 legal documents, belonging to the case categories of
either Murder, Land Dispute, Robbery, or Corruption, where phrases expressing
intent same as the category of the document are annotated. Also, we annotate
fine-grained intents for each such phrase to enable a deeper understanding of
the case for a reader. Finally, we analyze the performance of several
transformer-based models in automating the process of extracting intent phrases
(both at a coarse and a fine-grained level), and classifying a document into
one of the possible 4 categories, and observe that, our dataset is challenging,
especially in the case of fine-grained intent classification.
