  We critically assess mainstream accounting and finance research applying
methods from computational linguistics (CL) to study financial discourse. We
also review common themes and innovations in the literature and assess the
incremental contributions of work applying CL methods over manual content
analysis. Key conclusions emerging from our analysis are: (a) accounting and
finance research is behind the curve in terms of CL methods generally and word
sense disambiguation in particular; (b) implementation issues mean the proposed
benefits of CL are often less pronounced than proponents suggest; (c)
structural issues limit practical relevance; and (d) CL methods and high
quality manual analysis represent complementary approaches to analyzing
financial discourse. We describe four CL tools that have yet to gain traction
in mainstream AF research but which we believe offer promising ways to enhance
the study of meaning in financial discourse. The four tools are named entity
recognition (NER), summarization, semantics and corpus linguistics.
