Event Linking: Grounding Event Mentions to W ikipedia (2023)

Abstract: Comprehending an article requires understanding its constituent events. However, the context where an event is mentioned often lacks the details of this event. A question arises: how can the reader obtain more knowledge about this particular event in addition to what is provided by the local context in the article? This work defines Event Linking, a new natural language understanding task at the event level. Event linking tries to link an event mention appearing in an article to the most appropriate Wikipedia page. This page is expected to provide rich knowledge about what the event mention refers to. To standardize the research in this new direction, we contribute in four-fold. First, this is the first work in the community that formally defines the Event Linking task. Second, we collect a dataset for this new task. Specifically, we automatically gather the training set from Wikipedia, and then create two evaluation sets: one from the Wikipedia domain, reporting the in-domain performance, and a second from the real-world news domain, to evaluate out-of-domain performance. Third, we retrain and evaluate two state-of-the-art (SOTA) entity linking models, showing the challenges of event linking, and we propose an event-specific linking system, EVELINK, to set a competitive result for the new task. Fourth, we conduct a detailed and insightful analysis to help understand the task and the limitations of the current model. Overall, as our analysis shows, Event Linking is a challenging and essential task requiring more effort from the community.

## introduction grounding is a process of disambiguation and knowledge acquisition, and is an important task for natural language understanding. entity linking, grounding entity mentions to a knowledge base 1 data and code are available here: http://cogcomp. org/page/publication_view/996 . two homemade pressure cooker bombs detonated in boston, killing 3 people and injuring hundreds of others. the police released the images of two suspects. ## grounding events in wikipedia given an article and an event mention m in it, event linking tries to find a title t, from all the english wikipedia titles (around 5m titles), to provide the best explanation of m. event mention is defined as verb or nominal that refers to an event. a correct title is defined as follows: as long as a wikipedia page is about this event, or any subsection of the page introduces this event, we regard its title as the correct one. in this paper, all the models assume gold event mentions are given. for each event mention, a system is expected to label it with the correct wikipedia page or a "nil" tag if the event does not exist in wikipedia. accuracy is adopted as the official evaluation metric. part of the manhattan bridge will be closed so that its roadway can be rebuilt. ## nfl draft the 2000 nfl draft was the procedure by which national football league teams selected amateur u.s. college football players. it is officially known as the nfl annual player selection meeting. ## data construction we collect training data and in-domain test data from wikipedia automatically, and manually annotate a test set in the news domain for out-of-domain evaluation purpose. ## model in this section, we propose evelink as the first event linking model. we first introduce the representation of event mentions and event titles in section 5.1, and then introduce the model architecture in section 5.2. ## experiments in this section, we evaluate the in-domain performance on wiki test set and the out-of-domain performance on nyt test set, and conduct an error analysis. implementation details in appendix a. baselines. since there is no existing event linking system, we have to compare with previous entity linking systems. in this paper, we mainly compare our system with two sota entity linking models blink @xcite and @xcite . to make a fair comparison, blink and genre have the following two setups: blink/genre-entity: since a large portion of event mentions are nominals, which is also a kind of entity, it would be interesting to see how a sota entity linking system performs for event linking. therefore, we test the blink/genre model pretrained specific to entity linking directly. please note that the size of entity linking training data is 9 million, which is much larger than the size of event linking training data 66k. blink/genre-event: it adopts the same algorithm with the original blink/genre system, but is trained on our event linking training set. for all the experiments, blink-entity retrieves 10 candidates from candidate generation, and both blink-event and evelink retrieves 100 candidates from candidate generation. these numbers are tuned on dev data. genre is a generation model, which does not use the same pipeline of candidate generation and ranking. we follow the original setting to use the beam search with 5 beams. besides sota entity linking systems, we also evaluate the performance of bm25, glove vector cosine similarity between event mention and titles @xcite and prior distribution. because event mentions are limited in wikipedia, to fairly estimate the prior distribution of the event titles, we only evaluate event mentions that appear at least 10 times in wikipedia. in-domain experiment on wikipedia. we evaluate evelink on the wikipedia test set as the in-domain performance. we report the recall of candidate generation in ## conclusion in this work, we formulate event linking, a challenging but essential task, with a carefully designed wikipedia dataset and nyt test set, and propose an event linking model evelink for future works.