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).

BUILD KNOWLEDGE GRAPH FROM HETEROGENEOUS DOCUMENTS

2021 ◽  
Vol 47 (05) ◽  
Author(s):  
NGUYỄN CHÍ HIẾU
Keyword(s):  
Information Retrieval ◽  
Deep Learning ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Question Answering ◽  
Semantic Analysis ◽  
Knowledge Graph ◽  
Question Answering Systems ◽  
Knowledge Graphs

Knowledge Graphs are applied in many fields such as search engines, semantic analysis, and question answering in recent years. However, there are many obstacles for building knowledge graphs as methodologies, data and tools. This paper introduces a novel methodology to build knowledge graph from heterogeneous documents.  We use the methodologies of Natural Language Processing and deep learning to build this graph. The knowledge graph can use in Question answering systems and Information retrieval especially in Computing domain

scholarly journals Embedding Web-Based Statistical Translation Models in Cross-Language Information Retrieval

2003 ◽  
Vol 29 (3) ◽  
pp. 381-419 ◽  
Author(s):  
Wessel Kraaij ◽  
Jian-Yun Nie ◽  
Michel Simard
Keyword(s):  
Information Retrieval ◽  
Low Cost ◽  
Standard Test ◽  
Query Translation ◽  
Web Based ◽  
Parallel Corpora ◽  
Cross Language Information Retrieval ◽  
Cross Language ◽  
Parallel Texts ◽  
The Web

Although more and more language pairs are covered by machine translation (MT) services, there are still many pairs that lack translation resources. Cross-language information retrieval (CLIR) is an application that needs translation functionality of a relatively low level of sophistication, since current models for information retrieval (IR) are still based on a bag of words. The Web provides a vast resource for the automatic construction of parallel corpora that can be used to train statistical translation models automatically. The resulting translation models can be embedded in several ways in a retrieval model. In this article, we will investigate the problem of automatically mining parallel texts from the Web and different ways of integrating the translation models within the retrieval process. Our experiments on standard test collections for CLIR show that the Web-based translation models can surpass commercial MT systems in CLIR tasks. These results open the perspective of constructing a fully automatic query translation device for CLIR at a very low cost.

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