We present the first corpus annotated with preposition supersenses, unlexicalized categories for semantic functions that can be marked by English prepositions . That scheme improves upon its predecessors to better facilitate comprehensive manual annotation. Moreover, unlike the previous schemes, the preposition supersenses are organized hierarchically. Our data will be publicly released on the web upon publication.
Claim detection and verification are crucial for news understanding and have emerged as promising technologies for mitigating misinformation in news. However, most existing work focus on analysis of claim sentences while overlooking crucial background attributes, such as the claimer, claim objects, and other knowledge connected to the claim. In this work, we present NEWSCLAIMS, a new benchmark for knowledge-aware claim detection in the news domain. We re-define the claim detection problem to include extraction of additional background attributes related to the claim and release 529 claims annotated over 103 news articles. In addition, NEWS-CLAIMS aims to benchmark claim detection systems in emerging scenarios, comprising unseen topics with little or no training data. Finally, we provide a comprehensive evaluation of various zero-shot and prompt-based baselines for this new benchmark 1 .
This paper describes the evolution of the Prop-Bank approach to semantic role labeling over the last two decades. During this time the Prop-Bank frame files have been expanded to include non-verbal predicates such as adjectives, prepositions and multi-word expressions. The number of domains, genres and languages that have been PropBanked has also expanded greatly, creating an opportunity for much more challenging and robust testing of the generalization capabilities of PropBank semantic role labeling systems. We also describe the substantial effort that has gone into ensuring the consistency and reliability of the various annotated datasets and resources, to better support the training and evaluation of such systems.
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