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Search for Answers in Domain-Specific Supported by Intelligent Agents


Affiliations
1 Computer Science, Universidad Autonoma de Puebla, Puebla, Mexico
2 Universidad Autónoma de Puebla, Puebla, Mexico
 

Search for answers in specific domains is a new milestone in question answering. Traditionally, question answering has focused on general domain questions. Thus, the most relevant answers (or passages) are selected according to the type of question and the Named Entities included in the possible answers. In this paper, we present a novel approach on question answering over specific (or technical) domains. This proposal allows us to answer questions such as “What article is appropriate for … “, “What are the articles related to … “, these kind of questions cannot be answered by a general question answering system. Our approach is based on a set of laws of a specific domain, which contain a large set of laws regarding the work organized into a hierarchy. We consider generic concepts such as “article” semantic categories. Our results on the corpus of Federal Labor Law show that this approach is effective and highly reliable.

Keywords

Question Answering, Search for Answers, Mobiles, Intelligent Agents, & Question Answering.
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  • PewResearchCenter (2013). Internet, Science & Tech. Informe de Pew Internet. Teens and technology 2013. http://www.pewinternet.org/2013/03/13.

  • Burger John, Cardie Claire, Chaudhri Vinay, Gaizauskas Robert, Harabagiu Sanda, Israel David, Jacquemin Christian, Lin Chin-Yew, Maiorano Steve, Miller George, Moldovan Dan, Ogden Bill, Prager John, Rilo+ Ellen, Singhal Amit, Shrihari Rohini, Strzalkowski Tomek, Voorhees Ellen, Weishedel Ralph. Issues, Tasks, and Program Structures to Roadmap Research in Question Answering (QA). Technical Report, National Institute of Standards and Technology.

  • Bakker, Dik, André Muller, Viveka Velupillai, Soren Wichmann, Cecil, H. Brown, Pamela Brown, Dmitry Egorov, Robert Mailhammer, Anthony Grant, and Eric W. Holman. (2009). Adding typology to lexicostatistics: a combined approach to language classification. Linguistic Typology 13: 167-179.

  • Junyeon Hwang, Myeong in Choi Tacklim Lee Seonki Jeon Seunghwan Kim Sounghoan Park Sehyun Park. Energy Prosumer Business Model Using Blockchain System to Ensure Transparency and Safety. Volume 141, December 2017, Pages 194-198. Energy Procedia, Elsevier.

  • Fernando Zacarias, Rosalba Cuapa, Antonio Sanchez and Iris Cerecedo (2011). Puebla in the palm of your hands. MoMM 2011: 260-263, ACM New York, USA. ISBN: 978-1-4503-0785-7.

  • Doug Cutting. (1999). http://lucene.apache.org/.

  • Nava Tovar A. (2015) Diccionario Jurídico, La institucionalización de la razón. Universidad Autónoma Metropolitana. http://www.diccionariojuridico.mx/. Anthropos Editorial.

  • Balderas Espinosa M.A. (2008) Master thesis. Faculty of Computer Science at Universidad Autónoma de Puebla.

  • Fernando Zacarias, Rosalba Cuapa, Guillermo De Ita, J.C. Acosta and Daniel Torres (2014). Planning Solutions in the Real World. International Journal of Artificial Intelligence & Applications. Vol. 5 N0. 3. IJAIA –AIRCC Publishing Corporation

  • Zhuo, Lyne, Colin, Gonzalo, Jian (2004). Domain-specific QA for the Constructor sector.


Abstract Views: 258

PDF Views: 147




  • Search for Answers in Domain-Specific Supported by Intelligent Agents

Abstract Views: 258  |  PDF Views: 147

Authors

Fernando Zacarias
Computer Science, Universidad Autonoma de Puebla, Puebla, Mexico
Rosalba Cuapa
Universidad Autónoma de Puebla, Puebla, Mexico
Guillermo De Ita
Computer Science, Universidad Autonoma de Puebla, Puebla, Mexico
Miguel Bracamontes
Computer Science, Universidad Autonoma de Puebla, Puebla, Mexico

Abstract


Search for answers in specific domains is a new milestone in question answering. Traditionally, question answering has focused on general domain questions. Thus, the most relevant answers (or passages) are selected according to the type of question and the Named Entities included in the possible answers. In this paper, we present a novel approach on question answering over specific (or technical) domains. This proposal allows us to answer questions such as “What article is appropriate for … “, “What are the articles related to … “, these kind of questions cannot be answered by a general question answering system. Our approach is based on a set of laws of a specific domain, which contain a large set of laws regarding the work organized into a hierarchy. We consider generic concepts such as “article” semantic categories. Our results on the corpus of Federal Labor Law show that this approach is effective and highly reliable.

Keywords


Question Answering, Search for Answers, Mobiles, Intelligent Agents, & Question Answering.

References