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Event Coreference Resolution (ECR) as a pairwise mention classification task is expensive both for automated systems and manual annotations. The task`s quadratic difficulty is exacerbated when using Large Language Models (LLMs), making prompt engineering for ECR prohibitively costly. In this work, we propose a graphical representation of events, X-AMR, anchored around individual mentions using a cross-document version of Abstract Meaning Representation. We then linearize the ECR with a novel multi-hop coreference algorithm over the event graphs. The event graphs simplify ECR, making it a) LLM cost-effective, b) compositional and interpretable, and c) easily annotated. For a fair assessment, we first enrich an existing ECR benchmark dataset with these event graphs using an annotator-friendly tool we introduce. Then, we employ GPT-4, the newest LLM by OpenAI, for these annotations. Finally, using the ECR algorithm, we assess GPT-4 against humans and analyze its limitations. Through this research, we aim to advance the state-of-the-art for efficient ECR and shed light on the potential shortcomings of current LLMs at this task. Code and annotations: https://github.com/ahmeshaf/gpt_coref 

This paper presents the first integration of PropBank role information into Wikidata, in order to provide a novel resource for information extraction, one combining Wikidata`s ontological metadata with PropBank`s rich argument structure encoding for event classes. We discuss a technique for PropBank augmentation to existing eventive Wikidata items, as well as identification of gaps in Wikidata`s coverage based on manual examination of over 11,300 PropBank rolesets. We propose five new Wikidata properties to integrate PropBank structure into Wikidata so that the annotated mappings can be added en masse. We then outline the methodology and challenges of this integration, including annotation with the combined resources. 

Despite continued interest in mixed-species groups, we still lack a unified understanding of how ecological and social processes work across scales to influence group formation. Recent work has revealed ecological correlates of mixed-species group formation, but the mechanisms by which concomitant social dynamics produce these patterns, if at all, is unknown. Here, we use camera trap data for six mammalian grazer species in Serengeti National Park. Building on previous work, we found that ecological variables, and especially forage quality, influenced the chances of species overlap over small spatio-temporal scales (i.e. on the scales of several metres and hours). Migratory species (gazelle, wildebeest and zebra) were more likely to have heterospecific partners available in sites with higher forage quality, but the opposite was true for resident species (buffalo, hartebeest and topi). These findings illuminate the circumstances under which mixed-species group formation is even possible. Next, we found that greater heterospecific availability was associated with an increased probability of mixed-species group formation in gazelle, hartebeest, wildebeest and zebra, but ecological variables did not further shape these patterns. Overall, our results are consistent with a model whereby ecological and social drivers of group formation are species-specific and operate on different spatio-temporal scales. This article is part of the theme issue ‘Mixed-species groups and aggregations: shaping ecological and behavioural patterns and processes’. 

This paper reports the first release of the UMR (Uniform Meaning Representation) data set. UMR is a graph-based meaning representation formalism consisting of a sentence-level graph and a document-level graph. The sentence-level graph represents predicate-argument structures, named entities, word senses, aspectuality of events, as well as person and number information for entities. The document-level graph represents coreferential, temporal, and modal relations that go beyond sentence boundaries. UMR is designed to capture the commonalities and variations across languages and this is done through the use of a common set of abstract concepts, relations, and attributes as well as concrete concepts derived from words from invidual languages. This UMR release includes annotations for six languages (Arapaho, Chinese, English, Kukama, Navajo, Sanapana) that vary greatly in terms of their linguistic properties and resource availability. We also describe on-going efforts to enlarge this data set and extend it to other genres and modalities. We also briefly describe the available infrastructure (UMR annotation guidelines and tools) that others can use to create similar data sets.