Structured Interpretation of Temporal Relations

Structured Interpretation of Temporal Relations Yuchen Zhang and Nianwen Xue Brandeis University 415 South Street, Waltham, MA yuchenz@brandeis.edu, xuen@brandeis.edu Abstract Temporal relations between events and time expressions in a document are often modeled in an unstructured manner where relations between individual pairs of time expressions and events are considered in isolation. This often results in inconsistent and incomplete annotation and computational modeling. We propose a novel annotation approach where events and time expressions in a document form a dependency tree in which each dependency relation corresponds to an instance of temporal anaphora where the antecedent is the parent and the anaphor is the child. We annotate a corpus of 235 documents using this approach in the two genres of news and narratives, with 48 documents doubly annotated. We report a stable and high inter-annotator agreement on the doubly annotated subset, validating our approach, and perform a quantitative comparison between the two genres of the entire corpus. We make this corpus publicly available. Keywords: Temporal Relation, Dependency Structure, Data Annotation 1. Introduction Understanding temporal relations between events and tem- poral expressions in a natural language text is a fundamen- tal part of understanding the meaning of text. Automatic detection of temporal relations also enhances downstream natural language applications such as story timeline con- struction, question answering, text summarization, infor- mation extraction, and others. Due to its potential, tem- poral relation detection has received a significant amount of interest in the NLP community in recent years. Most of the research attention has been devoted to defin- ing the “semantic” aspect of this problem – the identifica- tion of a set of semantic relations between pairs of events, between an event and a time expression, or between pairs of time expressions. Representative work in this vein in- cludes TimeML (Pustejovsky et al., 2003a), a rich tem- poral relation markup language that is based on and ex- tends Allen’s Interval Algebra (Allen, 1984). TimeML has been further enriched and extended for annotation in other domains (O’Gorman et al., 2016; Styler IV et al., 2014; Mostafazadeh et al., 2016). Corpora annotated with these schemes (Pustejovsky et al., 2003b; O’Gorman et al., 2016) are shown to have stable inter-annotator agreements, val- idating the temporal relations proposed in the TimeML. Through a series of TempEval shared tasks (Verhagen et al., 2007a; Verhagen et al., 2010a; UzZaman et al., 2012; Bethard et al., 2015; Bethard et al., 2016; Bethard et al., 2017), there has also been significant amount of research on building automatic systems aimed at predicting tempo- ral relations. Less attention, however, has been given to the “structural” aspect of temporal relation modeling – answering the ques- tion of which other events or time expressions a given time expression or event depends on for the interpretation of its temporal location. Having an answer to this question is important to both linguistic annotation and computational modeling. From the point of view of linguistic annotation, without an answer to this question, an annotator is faced with the choice of: …

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