Cross-cutting aspects of cross-language 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).

Advances in Library and Information Science - Advanced Methodologies and Technologies in Library Science, Information Management, and Scholarly Inquiry ◽

Analysis and Assessment of Cross-Language Question Answering Systems

10.4018/978-1-5225-7659-4.ch018 ◽

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 ◽

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).

Comparative Evaluation of Multilingual Information Access Systems - Lecture Notes in Computer Science ◽

A Cross–Language Question/Answering–System for German and English

10.1007/978-3-540-30222-3_54 ◽

2004 ◽

pp. 559-571 ◽

Cited By ~ 3

Author(s):

Günter Neumann ◽

Bogdan Sacaleanu

Keyword(s):

Question Answering ◽

Question Answering System ◽

Cross Language ◽

A terminological simplification transformation for natural language question-answering systems

10.3115/981131.981164 ◽

1986 ◽

Cited By ~ 4

Author(s):

David G. Stallard

Keyword(s):

Natural Language ◽

Question Answering ◽

Natural Language Question ◽

Multilingual Information Access for Text, Speech and Images - Lecture Notes in Computer Science ◽

Summarization Design for Interactive Cross-Language Question Answering

10.1007/11519645_36 ◽

2005 ◽

pp. 348-362

Author(s):

Daqing He ◽

Jianqiang Wang ◽

Jun Luo ◽

Douglas W. Oard

Keyword(s):

Question Answering ◽

Cross Language ◽

Multilingual Information Access for Text, Speech and Images - Lecture Notes in Computer Science ◽

Interactive Cross-Language Question Answering: Searching Passages Versus Searching Documents

10.1007/11519645_33 ◽

2005 ◽

pp. 323-333 ◽

Cited By ~ 2

Author(s):

Fernando López-Ostenero ◽

Julio Gonzalo ◽

Víctor Peinado ◽

Felisa Verdejo

Keyword(s):

Question Answering ◽

Cross Language ◽

A Machine Learning Approach for an Indonesian-English Cross Language Question Answering System

IEICE Transactions on Information and Systems ◽

10.1093/ietisy/e90-d.11.1841 ◽

2007 ◽

Vol E90-D (11) ◽

pp. 1841-1852 ◽

Cited By ~ 2

Author(s):

A. PURWARIANTI ◽

M. TSUCHIYA ◽

S. NAKAGAWA

Keyword(s):

Machine Learning ◽

Question Answering ◽

Learning Approach ◽

Question Answering System ◽

Machine Learning Approach ◽

Cross Language ◽

Question Classification in English-Chinese Cross-Language Question Answering: An Integrated Genetic Algorithm and Machine Learning Approach

2007 IEEE International Conference on Information Reuse and Integration ◽

10.1109/iri.2007.4296621 ◽

2007 ◽

Cited By ~ 11

Author(s):

Min-Yuh Day ◽

Chorng-Shyong Ong ◽

Wen-Lian Hsu

Keyword(s):

Machine Learning ◽

Genetic Algorithm ◽

Question Answering ◽

Learning Approach ◽

Machine Learning Approach ◽

Question Classification ◽

Cross Language ◽

Multilingual Information Access for Text, Speech and Images - Lecture Notes in Computer Science ◽

Experiments on Robust NL Question Interpretation and Multi-layered Document Annotation for a Cross–Language Question/Answering System

10.1007/11519645_41 ◽

2005 ◽

pp. 411-422 ◽

Cited By ~ 13

Author(s):

Günter Neumann ◽

Bogdan Sacaleanu

Keyword(s):

Question Answering ◽

Question Answering System ◽

Cross Language ◽

Language Question ◽

Document Annotation

Natural language question answering: the view from here

Natural Language Engineering ◽

10.1017/s1351324901002807 ◽

2001 ◽

Vol 7 (4) ◽

pp. 275-300 ◽

Cited By ~ 138

Author(s):

L. HIRSCHMAN ◽

R. GAIZAUSKAS

Keyword(s):

Natural Language ◽

Search Engines ◽

Question Answering ◽

Text Retrieval ◽

Everyday Language ◽

Natural Language Question ◽

On Line ◽

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.