Time expression (aka, timex) recognition and normalization (TERN) is a crucial task for
downstream research. However, previous studies have overlooked the critical …

We present three Natural Language Inference (NLI) challenge sets that can evaluate NLI
models on their understanding of temporal expressions. More specifically, we probe these …

Temporal expression identification is crucial for understanding texts written in natural
language. Although highly effective systems such as HeidelTime exist, their limited runtime …

Temporal Expression Extraction (TEE) is essential for understanding time in natural
language. It has applications in Natural Language Processing (NLP) tasks such as question …

Temporal expressions in text play a significant role in language understanding and correctly
identifying them is fundamental to various retrieval and natural language processing …

Humans often use expressions with vague terms which play a fundamental role for effective
communication. These expressions are successfully modeled with fuzzy technology, but …

Despite more than two decades of research on temporal tagging and temporal relation
extraction, usable tools for annotating text remain very basic and hard to set up from an …

Existing approaches to normalize time expressions highly rely on expert-engineered rules or
grammars, which are labor-intensive and difficult to scale to different scenarios. In this …

TimeML 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 …

Temporal and numerical expression understanding is of great importance in many
downstream Natural Language Processing (NLP) and Information Retrieval (IR) tasks …