Argument-Aware Approach To Event Linking (2024)

Abstract: Event linking connects event mentions in text with relevant nodes in a knowledge base (KB). Prior research in event linking has mainly borrowed methods from entity linking, overlooking the distinct features of events. Compared to the extensively explored entity linking task, events have more complex structures and can be more effectively distinguished by examining their associated arguments. Moreover, the information-rich nature of events leads to the scarcity of event KBs. This emphasizes the need for event linking models to identify and classify event mentions not in the KB as “out-of-KB,” an area that has received limited attention. In this work, we tackle these challenges by introducing an argument-aware approach. First, we improve event linking models by augmenting input text with tagged event argument information, facilitating the recognition of key information about event mentions. Subsequently, to help the model handle “out-of-KB” scenarios, we synthesize out-of-KB training examples from in-KB instances through controlled manipulation of event arguments. Our experiment across two test datasets showed significant enhancements in both in-KB and out-of-KB scenarios, with a notable 22% improvement in out-of-KB evaluations.

## introduction event linking @xcite involves associating mentions of events in text with corresponding nodes in a knowledge base (kb). this process of linking nodes can enhance text comprehension to facilitate various downstream applications, such as question-answering and recommendation systems @xcite @xcite . despite its significance, event linking remains a challenging and relatively under-explored task @xcite in 1775, the conflict between the british east india company and the maratha empire escalated into war. british troops under the command of colonel keating, left surat on 15 march 1775, for pune. ## problem statement event linking connects a mention of an event in the text to relevant entries within a knowledge base. if no matching entry exists, models predict "nil," as established in prior research @xcite . the event-linking model analyzes a sequence of tokens @xmath0, x 2 , ..., x eve , ..., x l , where x eve signifies the target event mention. its objective is to output y, which can be either an entry e i from the knowledge base (size n), or "nil" if no suitable match is found. ## method we hereby introduce our event linking model with two key innovations, as illustrated in fig. 2 . first, to help the model distinguish event attribute details, we propose to first tag the event argument information of the input query ( §4.1). second, to improve the model's capability to deal with out-of-kb cases, we introduce a negative data generation method that synthesizes potential out-of-kb examples to train our model ( §4.2). finally, we train the model with these data changes ( §4.3). ## conclusion in this work, we introduce an argument-aware method designed to improve event linking models. this approach aids in disambiguating events and generating out-of-kb training examples. experimental results demonstrate that our method enhances the accuracy for both in-kb and out-of-kb queries. our findings reveal that the system, trained on flattened data, struggles to process structured textual information effectively. therefore, implementing our guidance about event arguments can improve its understanding of structured events.