Annotating Causality in the TempEval-3 Corpus

Rachele Sprugnoli, P. Mirza, S. Tonelli, M. Speranza

Risultato della ricerca: Contributo in libroContributo a convegno

Abstract

While there is a wide consensus in the NLP community over the modeling of temporal relations between events, mainly based on Allen’s temporal logic, the question on how to annotate other types of event relations, in particular causal ones, is still open. In this work, we present some annotation guidelines to capture causality between event pairs, partly inspired by TimeML. We then implement a rule-based algorithm to automatically identify explicit causal relations in the TempEval-3 corpus. Based on this annotation, we report some statistics on the behavior of causal cues in text and perform a preliminary investigation on the interaction between causal and temporal relations.
Lingua originaleEnglish
Titolo della pubblicazione ospiteProceedings of the EACL 2014 Workshop on Computational Approaches to Causality in Language (CAtoCL)
Pagine10-19
Numero di pagine10
Stato di pubblicazionePubblicato - 2014
EventoEACL 2014 Workshop on Computational Approaches to Causality in Language (CAtoCL) - Gothenburg, SWEDEN
Durata: 26 apr 201426 apr 2014

Convegno

ConvegnoEACL 2014 Workshop on Computational Approaches to Causality in Language (CAtoCL)
CittàGothenburg, SWEDEN
Periodo26/4/1426/4/14

Keywords

  • annotation, temporal information processing, causality

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