Proceedings of the First NLPL Workshop on Deep Learning for Natural Language Processing

Quantifying the morphosyntactic content of Brown Clusters

SemEval-2019 Task 7: RumourEval 2019: Determining Rumour Veracity and Support for Rumours

Helping Crisis Responders Find the Informative Needle in the Tweet Haystack

IUCM at SemEval-2018 Task 11: Similar-Topic Texts as a Comprehension Knowledge Source

Proceedings of the 27th International Conference on Computational Linguistics

SemEval-2017 Task 8: RumourEval: Determining rumour veracity and support for rumours

Media is full of false claims. Even Oxford Dictionaries named “post-truth” as the word of 2016. This makes it more important than ever to build systems that can identify the veracity of a story, and the nature of the discourse around it. RumourEval …

SemEval-2015 Task 6: Clinical TempEval

Clinical TempEval 2015 brought the temporal information extraction tasks of past TempEval campaigns to the clinical domain. Nine sub-tasks were included, covering problems in time expression identification, event expression identification and …

Analysis of temporal expressions annotated in clinical notes

Annotating the semantics of time in language is important. THYME (Styler et al., 2014) is a recent temporal annotation standard for clinical texts. This paper examines temporal expressions in the first major corpus released under this standard. It …