SPINOZA_VU: An NLP Pipeline for Cross Document TimeLines

Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pages 787–791, Denver, Colorado, June 4-5, 2015. c⃝2015 Association for Computational Linguistics SPINOZA VU: An NLP Pipeline for Cross Document TimeLines Tommaso Caselli Antske Fokkens Roser Morante Computational Lexicology & Terminology Lab (CLTL)Piek Vossen VU Amsterdam, De Boelelaan 1105 1081 HV Amsterdam Nederland {t.caselli}{antske.fokkens}{r.morantevallejo}{p.t.j.m.vossen}@vu.nl Abstract This paper describes the system SPINOZA VU developed for the SemEval 2015 Task 4: Cross Document TimeLines. The system integrates output from the News- Reader Natural Language Processing pipeline and is designed following an entity based model. The poor performance of the submit- ted 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. 1 Introduction This paper reports on a system (SPINOZA VU) for timeline extraction developed at the CLTL Lab of the VU Amsterdam in the context of the SemEval 2015 Task 4: Cross Document TimeLines. In this task, a timeline is defined as a set of chronologically anchored and ordered events extracted from a corpus spanning over a (large) period of time with respect to a target entity. Cross-document timeline extraction benefits from previous works and evaluation campaigns in Tem- poral Processing, such as the TempEval evaluation campaigns (Verhagen et al., 2007; Verhagen et al., 2010; UzZaman et al., 2013) and aims at promoting research in temporal processing by tackling the fol- lowing issues: cross-document and cross-temporal event detection and ordering; event coreference (in- document and cross-document); and entity-based temporal processing. The SPINOZA VU system is based on the News- Reader (NWR) NLP pipeline (Agerri et al., 2013; Beloki et al., 2014), which has been developed within the context of the NWR project1 and pro- vides multi-layer annotations over raw texts from tokenization up to temporal relations. The goal of the NWR project is to build structured event in- dexes from large volumes of news data addressing the same research issues as the task. Within this framework, we are developing a storyline module which aims at providing more structured represen- tation of events and their relations. Timeline extrac- tion from raw text qualifies as the first component of this new module. This is why we participated in Track A and Subtrack A of the task, timeline extrac- tion from raw text. Participating in Track B would require a full re-engineering of the NWR pipeline and of our system. The remainder of the paper is structured as fol- lows: Section 2 provides an overview of the model implemented in the two versions of our system. Sec- tion 3 presents the results and error analysis, and Section 4 puts forward some conclusions. 2 From Model to System Timeline extraction involves a number of indepen- dent though highly connected subtasks, the most relevant ones being: entity resolution, event …

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