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.
Compared to previous works on Track A of the SemEval 2015 Timeline extraction task, our approach differs in several important ways. Firstly, it addresses the extraction of implicit information to provide a better time-anchoring . More specifically, we are inspired by recent works on Implicit Semantic Role Labelling (ISRL) and, specially, on @cite_11 who adapted ISRL to focus on modifiers, including temporal arguments, instead of core arguments or roles. Given that not training data is provided, we developed a deterministic algorithm for timeline extraction loosely inspired by @cite_4 . Secondly, we extend the monolingual approach to make it multi- and cross-lingual, which constitutes a novel system on its own. Finally, our approach outperforms every other previous approach on the task, almost doubling the score of the next best system.
@cite_4 @cite_11
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This paper presents a novel deterministic algorithm for implicit Semantic Role Labeling. The system exploits a very simple but relevant discursive property, the argument coherence over different instances of a predicate. The algorithm solves the implicit arguments sequentially, exploiting not only explicit but also the implicit arguments previously solved. In addition, we empirically demonstrate that the algorithm obtains very competitive and robust performances with respect to supervised approaches that require large amounts of costly training data. This paper presents a methodology to infer implicit semantic relations from verbargument structures. An annotation effort shows implicit relations boost the amount of meaning explicitly encoded for verbs. Experimental results with automatically obtained parse trees and verb-argument structures demonstrate that inferring implicit relations is a doable task.