| Article ID | Journal | Published Year | Pages | File Type |
|---|---|---|---|---|
| 557741 | Web Semantics: Science, Services and Agents on the World Wide Web | 2015 | 7 Pages |
Abstract
This paper presents a system that uses semantic data to improve cross-lingual linking of news article clusters. Two approaches are compared. The first based on two different Canonical Correlation Analysis (CCA) feature vector definitions: MAX-CCA and SUM-CCA, whereas the second one has been developed using a better-performed CCA approach in combination with Entity vectors. The aim of the comparison was to determine whether taking into account the semantic aspect of news increases performance and improves linking. Evaluations of the aforementioned techniques on a news corpus, both against Google News and manual, revealed good performance of our system. The overall gain in precision and recall when using entity vectors was significant.
Related Topics
Physical Sciences and Engineering
Computer Science
Information Systems
Authors
Evgenia Belyaeva, Aljaž Košmerlj, Andrej Muhič, Jan Rupnik, Flavio Fuart,