  In this paper an open-domain factoid question answering system for Polish,
RAFAEL, is presented. The system goes beyond finding an answering sentence; it
also extracts a single string, corresponding to the required entity. Herein the
focus is placed on different approaches to entity recognition, essential for
retrieving information matching question constraints. Apart from traditional
approach, including named entity recognition (NER) solutions, a novel
technique, called Deep Entity Recognition (DeepER), is introduced and
implemented. It allows a comprehensive search of all forms of entity references
matching a given WordNet synset (e.g. an impressionist), based on a previously
assembled entity library. It has been created by analysing the first sentences
of encyclopaedia entries and disambiguation and redirect pages. DeepER also
provides automatic evaluation, which makes possible numerous experiments,
including over a thousand questions from a quiz TV show answered on the grounds
of Polish Wikipedia. The final results of a manual evaluation on a separate
question set show that the strength of DeepER approach lies in its ability to
answer questions that demand answers beyond the traditional categories of named
entities.
