Comprehending an article requires understanding its constituent events. However, the context where an event is mentioned often lacks the details of this event. Then, where can we obtain more knowledge of this particular event in addition to its context? 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 a news article for example, to the most appropriate Wikipedia page. This page is expected to provide rich knowledge about what the event refers to. To standardize the research of this new problem, we contribute in three-fold. First, this is the first work in the community that formally defines event linking task. Second, we collect a dataset for this new task. In specific, we first gather training set automatically from Wikipedia, then create two evaluation sets: one from the Wikipedia domain as well, reporting the in-domain performance; the other from the real-world news domain, testing the out-of-domain performance. Third, we propose EVELINK, the first-ever Event Linking approach. Overall, event linking is a considerably challenging task requiring more efforts from the community. Data and code are available here: https://github.com/ CogComp/event-linking.
[1]
Ido Dagan,et al.
WEC: Deriving a Large-scale Cross-document Event Coreference dataset from Wikipedia
,
2021,
NAACL.
[2]
Ming-Wei Chang,et al.
Semantic Parsing via Staged Query Graph Generation: Question Answering with Knowledge Base
,
2015,
ACL.
[3]
Miguel Ballesteros,et al.
Linking Entities to Unseen Knowledge Bases with Arbitrary Schemas
,
2020,
NAACL.
[4]
Hans Uszkoreit,et al.
Event Linking with Sentential Features from Convolutional Neural Networks
,
2016,
CoNLL.
[5]
Jason Weston,et al.
Poly-encoders: Transformer Architectures and Pre-training Strategies for Fast and Accurate Multi-sentence Scoring
,
2019
.
[6]
Daniel S. Weld,et al.
Fine-Grained Entity Recognition
,
2012,
AAAI.
[7]
Joel Nothman,et al.
Event Linking: Grounding Event Reference in a News Archive
,
2012,
ACL.
[8]
Luke Zettlemoyer,et al.
Zero-shot Entity Linking with Dense Entity Retrieval
,
2019,
ArXiv.
[9]
Dan Roth,et al.
Entity Linking via Joint Encoding of Types, Descriptions, and Context
,
2017,
EMNLP.
[10]
Doug Downey,et al.
Local and Global Algorithms for Disambiguation to Wikipedia
,
2011,
ACL.
[11]
Ming-Wei Chang,et al.
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
,
2019,
NAACL.