Abstract

Given a question Q and a sentence/paragraph SP that is likely to contain the answer to Q, an answer selection module is supposed to select the “exact” answer sub-string A ⊂ SP. We study three distinct approaches to solving this problem: one approach uses algorithms that rely on rich knowledge bases and sophisticated syntactic/semantic processing; one approach uses patterns that are learned in an unsupervised manner from the web, using computational biology-inspired alignment algorithms; and one approach uses statistical noisy-channel algorithms similar to those used in machine translation. We assess the strengths and weaknesses of these three approaches and show how they can be combined using a maximum entropy-based framework.

Date

2008

Authors

Abdessamad Echihabi, Ulf Hermjakob, Eduard Hovy, Daniel Marcu, Eric Melz, Deepak Ravichandran

Journal

Advances in open domain question answering

Pages

383-406

Publisher

Springer Netherlands