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Computación y Sistemas

versión impresa ISSN 1405-5546

Comp. y Sist. vol.12 no.1 México jul./sep. 2008

 

Mexican Experience in Spanish Question Answering

 

Experiencia Mexicana en la Búsqueda de Respuestas en Español

 

Manuel Montes y Gómez, Luis Villaseñor Pineda and Aurelio López López

 

Laboratorio de Tecnologías del Lenguaje, Coordinación de Ciencias Computacionales, Instituto Nacional de Astrofísica, Óptica y Electrónica (INAOE). Luis Enrique Erro #1, Tonatzintla, Puebla, México. e–mails: mmontesg@inaoep.mx, villasen@inaoep.mx, allopez@inaoep.mx

 

Article received on April 14, 2008
Accepted on June 20, 2008

 

Abstract.

Nowadays, due to the great advances in communication and storage media, there is more information available than ever before. This information can satisfy almost every information need; nevertheless, without the appropriate manage facilities, all of it is practically useless. This fact has motivated the emergence of several text processing applications that help in accessing large document collections. Currently, there are three main approaches for this purpose: information retrieval, information extraction, and question answering. Question answering (QA) systems aim to identify the exact answer to a question from a given document collection. This paper presents a survey of the Mexican experience in Spanish QA. In particular, it presents an overview of the participations of the Language Technologies Laboratory of INAOE (LabTL) in the Spanish QA evaluation task at CLEF, from 2004 to 2007. Through these participations, the LabTL has mainly explored two different approaches for QA: a language independent approach based on statistical methods, and a language dependent approach supported by sophisticated linguistic analyses of texts. It is important to point out that, due to these works, the LabTL has become one of the leading research groups in Spanish QA.

Keywords: Question Answering, Passage Retrieval, Answer Extraction, Machine Learning.

 

Resumen.

En la actualidad, debido a los grandes avances en los medios de comunicación y de almacenamiento, hay más información disponible como nunca antes se ha visto. Esta información puede satisfacer casi todas las necesidades de información, sin embargo, sin una adecuada gestión ésta es prácticamente inútil. Este hecho ha motivado la aparición de diferentes aplicaciones para el procesamiento de texto orientadas a facilitar el acceso a grandes colecciones de documentos. Hoy en día, existen tres enfoques principales para este propósito: la recuperación de información, la extracción de información, y los sistemas de búsqueda de respuestas. Los sistemas de búsqueda de respuestas (QA por sus siglas en inglés) tienen por objeto identificar la respuesta exacta a una pregunta dentro de una determinada colección de documentos. Este trabajo presenta un panorama general de la experiencia mexicana en QA en español. En particular, se presentan las participaciones del Laboratorio de Tecnologías del Lenguaje del INAOE (LabTL) en la tarea de QA en español dentro del foro de evaluación CLEF, desde 2004 a 2007. A través de estas participaciones, el LabTL ha explorado principalmente dos enfoques diferentes en QA: un enfoque independiente del lenguaje basado en métodos estadísticos, y un enfoque dependiente del lenguaje apoyado en un complejo análisis lingüístico del texto. Es importante señalar que, debido a estos trabajos, el LabTL se ha convertido en uno de los principales grupos de investigación de QA en español.

Palabras Claves: Búsqueda de Respuestas, Recuperación de Pasajes, Extracción de Respuestas, Aprendizaje Automático.

 

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Acknowledgements

The authors want to recognize the direct contribution of doctoral students Alberto Téllez–Valero, Manuel Pérez–Coutiño, Rita M. Aceves–Pérez, and master students Antonio Juárez–González, Claudia Denicia–Carral, as well as the indirect efforts of doctoral students Thamar Solorio, René García–Hernández, and master students Gustavo Hernández–Rubio, Esaú Villatoro–Tello, Aarón Pancardo–Rodríguez, and Ma. del Rosario Peralta–Calvo. This research was partially supported by Conacyt through research grants C01–39957, 43990A–1, and SNI.

 

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