Event Sequencing Annotation with TIE - ML (2022)

Abstract: TIE-ML (Temporal Information Event Markup Language) first proposed by Cavar et al. (2021) provides a radically simplified temporal annotation schema for event sequencing and clause level temporal properties even in complex sentences. TIE-ML facilitates rapid annotation of essential tense features at the clause level by labeling simple or periphrastic tense properties, as well as scope relations between clauses, and temporal interpretation at the sentence level. This paper presents the first annotation samples and empirical results. The application of the TIE-ML strategy on the sentences in the Penn Treebank (Marcus et al., 1993) and other non-English language data is discussed in detail. The motivation, insights, and future directions for TIE-ML are discussed, too. The aim is to develop a more efficient annotation strategy and a formalism for clause-level tense and aspect labeling, event sequencing, and tense scope relations that boosts the productivity of tense and event-level corpus annotation. The central goal is to facilitate the production of large data sets for machine learning and quantitative linguistic studies of intra- and cross-linguistic semantic properties of temporal and event logic.

## 2 3 example (11) on the other hand exhibits a mismatch between the linear presentation sequence of sub-events and the underlying temporal alignment on the time axis. capturing the empirical sequencing of events along the time axis, their potential coincidence, or their overlap is relevant at various levels in semantically aware natural language processing (nlp) or artificial intelligence (ai) applications. in computational reasoning, the temporal sequence of events is often relevant for the analysis of causality or intention. the central focus in the first phase of this effort was to provide temporal sequencing annotations for events in complex sentences. there are many other highly relevant issues related to this, as for example duration of events. by breaking the temporal annotation effort and ml model training into atomic tasks, we expect to achieve faster much better results. in the next project phase we are extending the effort to annotation of duration and overlaps of events. we have not identified any other resource that could provide enough cross-linguistic data sufficiently large to train ml models and that would be able to provide temporal sequencing and duration annotations of events at the clause level. this is another motivation for the corpus project discussed here.