This paper presents an entity recognition (ER) module for a question answering system for Polish called RAFAEL. Two techniques of ER are compared: traditional, based on named entity categories (e.g. person), and novel Deep Entity Recognition, using WordNet synsets (e.g. impressionist). The latter is possible thanks to a previously assembled entity library, gathered by analysing encyclopaedia definitions. Evaluation based on over 500 questions answered on the grounds of Wikipedia suggests that the strength of DeepER approach lies in its ability to tackle questions that demand answers beyond the categories of named entities.
CITATION STYLE
Przybyła, P. (2015). Gathering knowledge for question answering beyond named entities. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9103, pp. 412–417). Springer Verlag. https://doi.org/10.1007/978-3-319-19581-0_39
Mendeley helps you to discover research relevant for your work.