Difference between revisions of "What’s New? Analysing Language-Specific Wikipedia Entity Contexts to Support Entity-Centric News Retrieval"
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| + | | title = What’s New? Analysing Language-Specific Wikipedia Entity Contexts to Support Entity-Centric News Retrieval | ||
| + | | date = 2017 | ||
| + | | authors = [[Yiwei Zhou]]<br />[[Elena Demidova]]<br />[[Alexandra I. Cristea]] | ||
| + | | doi = 10.1007/978-3-319-59268-8_10 | ||
| + | | link = https://link.springer.com/chapter/10.1007/978-3-319-59268-8_10 | ||
| + | }} | ||
'''What’s New? Analysing Language-Specific Wikipedia Entity Contexts to Support Entity-Centric News Retrieval''' - scientific work related to [[Wikipedia quality]] published in 2017, written by [[Yiwei Zhou]], [[Elena Demidova]] and [[Alexandra I. Cristea]]. | '''What’s New? Analysing Language-Specific Wikipedia Entity Contexts to Support Entity-Centric News Retrieval''' - scientific work related to [[Wikipedia quality]] published in 2017, written by [[Yiwei Zhou]], [[Elena Demidova]] and [[Alexandra I. Cristea]]. | ||
== Overview == | == Overview == | ||
Representation of influential entities, such as celebrities and multinational corporations on the web can vary across languages, reflecting language-specific entity aspects, as well as divergent views on these entities in different communities. An important source of [[multilingual]] background knowledge about influential entities is [[Wikipedia]]—an online community-created encyclopaedia—containing more than 280 language editions. Such language-specific information could be applied in entity-centric [[information retrieval]] applications, in which users utilise very simple queries, mostly just the entity names, for the relevant documents. In this article authors focus on the problem of creating language-specific entity contexts to support entity-centric, language-specific information retrieval applications. First, authors discuss alternative ways such contexts can be built, including Graph-based and Article-based approaches. Second, authors analyse the similarities and the differences in these contexts in a case study including 219 entities and five Wikipedia language editions. Third, authors propose a context-based entity-centric information retrieval model that maps documents to aspect space, and apply language-specific entity contexts to perform query expansion. Last, authors perform a case study to demonstrate the impact of this model in a news retrieval application. Authors study illustrates that the proposed model can effectively improve the recall of entity-centric information retrieval while keeping high precision, and provide language-specific results. | Representation of influential entities, such as celebrities and multinational corporations on the web can vary across languages, reflecting language-specific entity aspects, as well as divergent views on these entities in different communities. An important source of [[multilingual]] background knowledge about influential entities is [[Wikipedia]]—an online community-created encyclopaedia—containing more than 280 language editions. Such language-specific information could be applied in entity-centric [[information retrieval]] applications, in which users utilise very simple queries, mostly just the entity names, for the relevant documents. In this article authors focus on the problem of creating language-specific entity contexts to support entity-centric, language-specific information retrieval applications. First, authors discuss alternative ways such contexts can be built, including Graph-based and Article-based approaches. Second, authors analyse the similarities and the differences in these contexts in a case study including 219 entities and five Wikipedia language editions. Third, authors propose a context-based entity-centric information retrieval model that maps documents to aspect space, and apply language-specific entity contexts to perform query expansion. Last, authors perform a case study to demonstrate the impact of this model in a news retrieval application. Authors study illustrates that the proposed model can effectively improve the recall of entity-centric information retrieval while keeping high precision, and provide language-specific results. | ||
Revision as of 09:24, 20 October 2019
Authors | Yiwei Zhou Elena Demidova Alexandra I. Cristea |
|---|---|
Publication date | 2017 |
DOI | 10.1007/978-3-319-59268-8_10 |
Links | Original |
What’s New? Analysing Language-Specific Wikipedia Entity Contexts to Support Entity-Centric News Retrieval - scientific work related to Wikipedia quality published in 2017, written by Yiwei Zhou, Elena Demidova and Alexandra I. Cristea.
Overview
Representation of influential entities, such as celebrities and multinational corporations on the web can vary across languages, reflecting language-specific entity aspects, as well as divergent views on these entities in different communities. An important source of multilingual background knowledge about influential entities is Wikipedia—an online community-created encyclopaedia—containing more than 280 language editions. Such language-specific information could be applied in entity-centric information retrieval applications, in which users utilise very simple queries, mostly just the entity names, for the relevant documents. In this article authors focus on the problem of creating language-specific entity contexts to support entity-centric, language-specific information retrieval applications. First, authors discuss alternative ways such contexts can be built, including Graph-based and Article-based approaches. Second, authors analyse the similarities and the differences in these contexts in a case study including 219 entities and five Wikipedia language editions. Third, authors propose a context-based entity-centric information retrieval model that maps documents to aspect space, and apply language-specific entity contexts to perform query expansion. Last, authors perform a case study to demonstrate the impact of this model in a news retrieval application. Authors study illustrates that the proposed model can effectively improve the recall of entity-centric information retrieval while keeping high precision, and provide language-specific results.
