Multi-lingual and Cross-lingual timeline extraction

In this paper we present an approach to extract ordered timelines of events, their participants, locations and times from a set of Multilingual and Cross-lingual data sources. Based on the assumption that event-related information can be recovered from different documents written in different langua...

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Bibliographic Details
Published in:Knowledge-based systems 2017-10, Vol.133, p.77-89
Main Authors: Laparra, Egoitz, Agerri, Rodrigo, Aldabe, Itziar, Rigau, German
Format: Article
Language:English
Subjects:
Algorithms
Anchors
Cross-document event coreference
Event ordering
Languages
Multilingualism
Natural Language Processing
Predicate Matrix
Reproducibility
Temporal processing
Time
Timeline extraction
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Summary:In this paper we present an approach to extract ordered timelines of events, their participants, locations and times from a set of Multilingual and Cross-lingual data sources. Based on the assumption that event-related information can be recovered from different documents written in different languages, we extend the Cross-document Event Ordering task presented at SemEval 2015 by specifying two new tasks for, respectively, Multilingual and Cross-lingual timeline extraction. We then develop three deterministic algorithms for timeline extraction based on two main ideas. First, we address implicit temporal relations at document level since explicit time-anchors are too scarce to build a wide coverage timeline extraction system. Second, we leverage several multilingual resources to obtain a single, interoperable, semantic representation of events across documents and across languages. The result is a highly competitive system that strongly outperforms the current state-of-the-art. Nonetheless, further analysis of the results reveals that linking the event mentions with their target entities and time-anchors remains a difficult challenge. The systems, resources and scorers are freely available to facilitate its use and guarantee the reproducibility of results.
ISSN:0950-7051
1872-7409
DOI:10.1016/j.knosys.2017.07.002