Model-driven Development for Adapting Question Answering Systems to Restricted Domains

A Question Answering (QA) system must provide concise answers from large collections of documents to questions stated by the user in natural language. However, although many QA systems for open domain exist, its adaptation for restricted domains (such as those of healthcare, agri culture, transportation, or science) is a far from trivial task. The principal problem is that domain experts ask more precise questions (and expect more precise answers), including specific terminology, and this is costly to integrate into a QA system. To overcome this drawback, this paper presents an innovative approach based on model-driven software development. It uses restricted-domain resources to automatically and effortlessly adapt open-domain QA systems in order to make them useful in restricted-domain scenarios. Finally, a set of experiments has been carried out to show the approach’s applicability.

[1]  José Luis Vicedo González,et al.  Addressing ontology-based question answering with collections of user queries , 2009, Inf. Process. Manag..

[2]  Sanda M. Harabagiu,et al.  Performance issues and error analysis in an open-domain question answering system , 2003, TOIS.

[3]  Daniel Ferrés,et al.  Experiments Adapting an Open-Domain Question Answering System to the Geographical Domain Using Scope-Based Resources , 2006 .

[4]  Gail Hodge,et al.  Systems of Knowledge Organization for Digital Libraries: Beyond Traditional Authority Files , 2000 .

[5]  Jennifer Chu-Carroll,et al.  Building Watson: An Overview of the DeepQA Project , 2010, AI Mag..

[6]  Bran Selic,et al.  The Pragmatics of Model-Driven Development , 2003, IEEE Softw..

[7]  W. Bruce Croft,et al.  Analysis of Statistical Question Classification for Fact-Based Questions , 2005, Information Retrieval.

[8]  Gosse Bouma,et al.  Developing Offline Strategies for Answering Medical Questions , 2005 .

[9]  P. Gorman,et al.  A taxonomy of generic clinical questions: classification study , 2000, BMJ : British Medical Journal.

[10]  Satoshi Sekine,et al.  Extended Named Entity Hierarchy , 2002, LREC.

[11]  Leila Kosseim,et al.  Improving the performance of question answering with semantically equivalent answer patterns , 2008, Data Knowl. Eng..

[12]  Antonio Ferrández Rodríguez,et al.  Using AliQAn in Monolingual QA@CLEF 2008 , 2008, CLEF.

[13]  Diego Molla Aliod,et al.  Question Answering in Restricted Domains: An Overview , 2007, CL.

[14]  Rafael Valencia-García,et al.  Modelling Reusable Security Requirements based on an Ontology Framework , 2009, J. Res. Pract. Inf. Technol..

[15]  Rafael Valencia-García,et al.  OWLPath: An OWL Ontology-Guided Query Editor , 2011, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.

[16]  Eduard Hovy,et al.  A question/answer typology with surface text patterns , 2002 .

[17]  Anneke Kleppe,et al.  MDA explained - the Model Driven Architecture: practice and promise , 2003, Addison Wesley object technology series.

[18]  Jean Bézivin,et al.  On the unification power of models , 2005, Software & Systems Modeling.

[19]  Anselmo Peñas,et al.  Overview of ResPubliQA 2009: Question Answering Evaluation over European Legislation , 2009, CLEF.

[20]  Dan Roth,et al.  Learning question classifiers: the role of semantic information , 2005, Natural Language Engineering.

[21]  Enrico Motta,et al.  AquaLog: An ontology-driven question answering system for organizational semantic intranets , 2007, J. Web Semant..

[22]  Sanda M. Harabagiu,et al.  Performance Issues and Error Analysis in an Open-Domain Question Answering System , 2002, ACL.