scholarly journals Use of Free On-Line Machine Translation for Interactive Cross-Language Question Answering

2006 ◽  
pp. 263-272 ◽  
Author(s):  
Angel Zazo ◽  
Carlos G. Figuerola ◽  
José Luis A. Berrocal ◽  
Viviana Fernández Marcial
Keyword(s):  
Machine Translation ◽  
Question Answering ◽  
On Line ◽  
Cross Language ◽  
Language Question

Analysis and Assessment of Cross-Language Question Answering Systems

2018 ◽  
pp. 4471-4479
Author(s):  
Juncal Gutiérrez-Artacho ◽  
María-Dolores Olvera-Lobo
Keyword(s):  
Information Retrieval ◽  
Applied Research ◽  
Question Answering ◽  
General Domain ◽  
Linguistic Resources ◽  
Question Answering Systems ◽  
Cross Language ◽  
Language Question ◽  
The Web

Within the sphere of the Web, the overload of information is more notable than in other contexts. Question answering systems (QAS) are presented as an alternative to the traditional Information Retrieval (IR) systems, seeking to offer precise and understandable answers to factual questions instead of showing the user a list of documents related to a given search . Given that the QAS is presented as a substantial advance in the improvement of IR, it becomes necessary to determine its effectiveness for the final user. With this aim, 7 studies were undertaken to evaluate: a) in the first two, the linguistic resources and tools used in these systems for multilingual retrieval (Research 1; Research 2); and b) the performance and quality of the answers of the main monolingual and multilingual QA of general domain and specialized domain in the Web in response to different types of questions and subjects, so that different evaluation means can be applied (Research 3, Research 4, Research 5, Research 6, Research 7).

Analysis and Assessment of Cross-Language Question Answering Systems

2019 ◽  
pp. 226-236
Author(s):  
Juncal Gutiérrez-Artacho ◽  
María-Dolores Olvera-Lobo
Keyword(s):  
Information Retrieval ◽  
Applied Research ◽  
Question Answering ◽  
General Domain ◽  
Linguistic Resources ◽  
Question Answering Systems ◽  
Cross Language ◽  
Language Question ◽  
The Web

Within the sphere of the web, the overload of information is more notable than in other contexts. Question answering systems (QAS) are presented as an alternative to the traditional information retrieval (IR) systems seeking to offer precise and understandable answers to factual questions instead of showing the user a list of documents related to a given search. Given that the QAS is presented as a substantial advance in the improvement of IR, it becomes necessary to determine its effectiveness for the final user. With this aim, seven studies were undertaken to evaluate: 1) in the first two, the linguistic resources and tools used in these systems for multilingual retrieval (Research 1, Research 2), and 2) the performance and quality of the answers of the main monolingual and multilingual QA of general domain and specialized domain in the web in response to different types of questions and subjects, so that different evaluation means can be applied (Research 3, Research 4, Research 5, Research 6, Research 7).

Natural language question answering: the view from here

2001 ◽  
Vol 7 (4) ◽  
pp. 275-300 ◽  
Author(s):  
L. HIRSCHMAN ◽  
R. GAIZAUSKAS
Keyword(s):  
Natural Language ◽  
Search Engines ◽  
Question Answering ◽  
Text Retrieval ◽  
Everyday Language ◽  
Question Answering Systems ◽  
Natural Language Question ◽  
On Line ◽  
Language Question

As users struggle to navigate the wealth of on-line information now available, the need for automated question answering systems becomes more urgent. We need systems that allow a user to ask a question in everyday language and receive an answer quickly and succinctly, with sufficient context to validate the answer. Current search engines can return ranked lists of documents, but they do not deliver answers to the user.Question answering systems address this problem. Recent successes have been reported in a series of question-answering evaluations that started in 1999 as part of the Text Retrieval Conference (TREC). The best systems are now able to answer more than two thirds of factual questions in this evaluation.

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