TimeBank
Related Papers
Using Semantic Networks to Identify Temporal Expressions from Semantic Roles
Nowadays, the temporal aspects of natural lan- guage are receiving a great research interest. TimeML has been adopted as a standard for tem- poral information annotation by a large number …...
TRIOS-TimeBank Corpus: Extended TimeBank Corpus with Help of Deep Understanding of Text
AbstractTimeBank (Pustejovsky et al, 2003a), a reference for TimeML (Pustejovsky et al, 2003b) compliant annotation, is widely used temporally annotated corpus in the community. It captures time expressions, events, and …...
Temporal Relation Classification in Persian and English contexts
This paper introduces the first pattern-based Persian Temporal Relation Classifier (PTRC) that finds the type of temporal relations between pairs of events in the Persian texts. The proposed system uses …...
Dense Event Ordering with a Multi-Pass Architecture
AbstractThe past 10 years of event ordering research has focused on learning partial orderings over document events and time expressions. The most popular corpus, the TimeBank, contains a small subset …...
Temporal Common Sense Acquisition with Minimal Supervision
AbstractTemporal common sense (e.g., duration and frequency of events) is crucial for understanding natural language. However, its acquisition is challenging, partly because such information is often not expressed explicitly in …...
Unsupervised Domain Adaptation for Event Detection using Domain-specific Adapters
Due to the multi-dimensional variation of tex- tual data, detection of event triggers from new domains can become a lot more challenging. This prompts a need to research on domain …...
A Comprehensive Evaluation and Correction of the TimeBank Corpus
AbstractTimeML is an annotation scheme for capturing temporal information in text. The developers of TimeML built the TimeBank corpus to both validate the scheme and provide a rich dataset of …...
Improving Event Duration Question Answering by Leveraging Existing Temporal Information Extraction Data
AbstractUnderstanding event duration is essential for understanding natural language. However, the amount of training data for tasks like duration question answering, i.e., McTACO, is very limited, suggesting a need for …...