  Studies in Referring Expression Generation (REG) often make use of corpora of
definite descriptions produced by human subjects in controlled experiments.
Experiments of this kind, which are essential for the study of reference
phenomena and many others, may however include a considerable amount of noise.
Human subjects may easily lack attention, or may simply misunderstand the task
at hand and, as a result, the elicited data may include large proportions of
ambiguous or ill-formed descriptions. In addition to that, REG corpora are
usually collected for the study of semantics-related phenomena, and it is often
the case that the elicited descriptions (and their input contexts) need to be
annotated with their corresponding semantic properties. This, as in many other
fields, may require considerable time and skilled annotators. As a means to
tackle both kinds of difficulties - poor data quality and high annotation costs
- this work discusses a semi-automatic method for the annotation of definite
descriptions produced by human subjects in REG data collection experiments. The
method makes use of simple rules to establish associations between words and
meanings, and is intended to facilitate the design of experiments that produce
REG corpora.
