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, inter-operable, 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.
Track A received three runs from two participants: the WHUNLP and SPINOZAVU teams. Both approaches were based on applying a pipeline of linguistic processors including Named Entity Recognition, Event and Nominal Coreference Resolution, Named Entity Disambiguation, and temporal processing . The SPINOZAVU system was further developed in @cite_18 .
@cite_18
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This paper describes the system SPINOZA VU developed for the SemEval 2015 Task 4: Cross Document TimeLines. The system integrates output from the NewsReader Natural Language Processing pipeline and is designed following an entity based model. The poor performance of the submitted runs are mainly a consequence of error propagation. Nevertheless, the error analysis has shown that the interpretation module behind the system performs correctly. An out of competition version of the system has fixed some errors and obtained competitive results. Therefore, we consider the system an important step towards a more complex task such as storyline extraction.