@inproceedings{f0f715f31a8142968b4fb27e3e72559c,
title = "Learning an optimal sequence of questions for the disambiguation of queries over structured data",
abstract = "Intelligent systems interacting with users often need to relate ambiguous natural language phrases lo formal entities which can be further processed. This work strives for learning an optimal sequence of disambiguation questions asked by an agent in order to achieve a perfect interactive disambiguation, setting itself off against previous work on interactive and adaptive dialogue systems for disambiguation in question answering. To this aim, we built a hybrid system that exhibits deductive and statistical inference capabilities by combining techniques from natural language processing, information retrieval, answer set programming and relational reinforcement learning.",
keywords = "Disambiguation, Information retrieval, Linked Data, Named-Entity recognition, Question answering, Relational reinforcement learning",
author = "Achim Rettingerf and Alexander Hagemann and Matthias Nickles",
note = "Publisher Copyright: {\textcopyright} Copyright 2014, Association for the Advancement of Artificial Intelligence (www.aaia.org). All rights reserved.; 28th AAAI Conference on Artificial Intelligence, AAAI 2014 ; Conference date: 28-07-2014",
year = "2014",
language = "English",
series = "AAAI Workshop - Technical Report",
publisher = "AI Access Foundation",
pages = "27--30",
booktitle = "Machine Learning for Interactive Systems",
}