  This paper explores the task of automatic prediction of text spans in a legal
problem description that support a legal area label. We use a corpus of problem
descriptions written by laypeople in English that is annotated by practising
lawyers. Inherent subjectivity exists in our task because legal area
categorisation is a complex task, and lawyers often have different views on a
problem, especially in the face of legally-imprecise descriptions of issues.
Experiments show that training on majority-voted spans outperforms training on
disaggregated ones.
