# Within-Document Arabic Event Coreference: Challenges, Datasets, Approaches and Future Direction
## 1 Introduction  
In the context of the ACE [1] and TimeML [2] schemas, an event is a significant occurrence or activity that happens at a specific point in time. However, the definitions of an event in these two schemas are slightly different. In the ACE2005 schema, an event is defined as a type of entity that represents a happening or occurrence that takes place at a particular time, such as a meeting, an attack, or an election. An event in ACE2005 is often characterized by attributes such as its type, subtype, and participants. In contrast, the TimeML schema defines an event as a concept that represents a single point or interval in time where something happens. TimeML events can include specific occurrences, such as an earthquake or a meeting, as well as more abstract events, such as the start or end of a period of time. Overall, both ACE2005 and TimeML define an event as a significant occurrence that takes place at a specific time, but the distinction between the two lies in how they represent and categorize events. ACE2005 is more focused on the type and attributes of events, while TimeML is more concerned with the temporal relationships between events and other temporal expressions in a text. In Table 1, bolded words are annotated as events in both corpora.  
Event coreference resolution is a task in Natural Language Processing (NLP) that involves identifying expressions (i.e., words) in a text that refer to the same real-world event. In other words, event coreference aims to find all the different ways that an event is mentioned in a text and link them together, so that NLP systems can better understand the relationship between events and the entities that participate in them. In Table 2, all bolded words are coreferencing events.  
Event coreference is important for natural language processing because it affects how well we can do things like summarizing, translating, and understanding texts. For instance, if we want to summarize a text, we need to know which events are the most important and how they are related to each other. Event coreference is also difficult because it requires knowing how humans make sense of and communicate events before using words. Humans can process the text directly without focusing on which event refers to another because they have a rich mental representation of events that is based on their perception, memory, and knowledge. They can use cues such as tense, aspect, modality, and discourse markers to infer the relations between events. However, these cues are not always explicit or consistent in natural language, and they may vary across languages and genres. This paper concentrates on event coreference for Arabic, which has more challenges and less research than event coreference for other languages. Arabic is a morphologically rich and syntactically complex language that has many variations and dialects. It also has different writing systems and conventions that affect how events are expressed in texts. This paper surveys the existing works on Arabic event coreference and challenges as well as available datasets. Furthermore, the paper proposes a schema for annotating Arabic event coreference based on the ACE (Automatic Content Extraction) Arabic Annotation Guidelines for Events [3].