Learning of user formulations for business listings in automatic directory assistance

Automatic Directory Assistance (DA) for business listings poses many application specific problems. One of the main problem is that customers formulate their requests for the same listing with a great variability. This paper presents the results of a study aiming at the evaluation of an approach towards automatic learning, from field data, of expressions typically used by customers to formulate their requests for the most frequent business listings. We use a clustering procedure that exploits the association of the phonetic string produced by a lexical unconstrained search for a given denomination pronounced by the user and the phone number provided by the system or by the human operator, in case of failure of the automatic DA service. We show that an unsupervised approach allows to detect user formulations that were not foreseen by the designers, and that can be added, as variants, to the denominations already included in the system to reduce its failures.