Abstract:
Those who develop Natural Language Processing
(NLP) systems sometimes find it convenient to develop several
representations of knowledge. Especially in question and
answer generating machines it is more logical to have several
knowledge bases (KB) to answer each specific types of
questions. This paper discusses a statistical learning based
algorithm to port a specific question to a desired KB. The
algorithm allows selection of KBs based on previously learnt
patterns. Due to probabilistic parsing and POS (Part of
Speech) tagging makes this algorithm much suitable for short
questions or short input sentences.
