Automatic knowledge graph construction aims at manufacturing structured human
knowledge. To this end, much effort has historically been spent extracting informative fact …

Relation extraction (RE) involves identifying the relations between entities from underlying
content. RE serves as the foundation for many natural language processing (NLP) and …

Temporal common sense (eg, duration and frequency of events) is crucial for understanding
natural language. However, its acquisition is challenging, partly because such information is …

We propose TRACIE, a novel temporal reasoning dataset that evaluates the degree to which
systems understand implicit events--events that are not mentioned explicitly in natural …

We present TIMERS-a TIME, Rhetorical and Syntactic-aware model for document-level
temporal relation classification in the English language. Our proposed method leverages …

Extracting various types of event relations in financial texts can benefit many downstream
applications supporting financial analysis. This paper addresses the multi-type event …

Understanding temporal commonsense concepts, such as times of occurrence and
durations is crucial for event-centric language understanding. Reasoning about such …

We introduce five new natural language inference (NLI) datasets focused on temporal
reasoning. We recast four existing datasets annotated for event duration—how long an …

We present LOME, a system for performing multilingual information extraction. Given a text
document as input, our core system identifies spans of textual entity and event mentions with …

Linear temporal logic (LTL) is commonly used in model checking tasks; moreover, it is well-
suited for the formalization of technical requirements. However, the correct specification and …