| Challenge: | a new task for natural language understanding is called Event Linking . the context where an event is mentioned lacks the details of this event . |
| Approach: | They propose a new task to link an article's event mention to the most appropriate Wikipedia page . they collect a training set from Wikipedia and evaluate two models to test the task . |
| Outcome: | The proposed model is based on a dataset and a real-world news domain . it is expected that the most appropriate Wikipedia page will provide rich knowledge about the mention . |
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Argument-Aware Approach To Event Linking (2024.findings-acl)
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| Challenge: | Prior research in event linking has mainly borrowed methods from entity linking, overlooking distinct features of events. |
| Approach: | They propose an argument-aware method to improve event linking models by augmenting input text with tagged event argument information. |
| Outcome: | The proposed method improves in-KB and out-of-KB queries and training examples. |
EventWiki: A Knowledge Base of Major Events (L18-1)
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| Challenge: | Existing knowledge bases focus on static entities such as people, locations and organizations. |
| Approach: | They propose a new knowledge base resource called EventWiki which concentrates on major events . they show that EventWiki is a very useful resource for information extraction regarding events in NLP . |
| Outcome: | The proposed resource is the first knowledge base resource of major events. |
Conundrums in Event Coreference Resolution: Making Sense of the State of the Art (2021.emnlp-main)
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| Challenge: | Recent years have seen the successful application of span-based neural models to entity-based information extraction tasks such as entity coreference resolution (CR) Existing event coreference resolvers focused on feature engineering are few and far between, let alone event corefers. |
| Approach: | They propose to adapt existing span-based event reference systems to event coreference by adapting the models originally developed for entity coreference to event CR. |
| Outcome: | The proposed model improves the representations of entity mentions in entity-based IE tasks compared to non-span models . |
Improving Entity Linking by Modeling Latent Relations between Mentions (P18-1)
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| Challenge: | Entity linking systems often exploit relations between textual mentions to decide if the linking decisions are compatible. |
| Approach: | They treat relations as latent variables while optimizing the neural entity-linking model without supervision. |
| Outcome: | The proposed model outperforms its relation-agnostic version and significantly outperformed its relational version. |
An AMR-based Link Prediction Approach for Document-level Event Argument Extraction (2023.acl-long)
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| Challenge: | Recent work has introduced Abstract Meaning Representation (AMR) for Document-level Event Argument Extraction (Doc-level EAE) however, in these works AMR is used only implicitly, for instance, as additional features or training signals. |
| Approach: | They propose a novel AMR-based graph structure which uses graph neural networks to find event arguments from unstructured text. |
| Outcome: | The proposed graph structure outperforms the state-of-the-art models by 3.63pt and 2.33pt F1 and reduces inference time by 56%. |
Multi-Sentence Argument Linking (2020.acl-main)
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| Challenge: | Existing datasets for cross-sentence linking are small, resulting in a lack of a model for argument linking. |
| Approach: | They propose a document-level model for finding argument spans that fill an event’s roles by combining semantic role labeling and coreference resolution. |
| Outcome: | The proposed model is able to connect arguments in sentence-level role labeling and coreference resolution on 9,124 annotated events across 139 types. |
Boosting Entity Linking Performance by Leveraging Unlabeled Documents (P19-1)
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| Challenge: | a new approach to entity linking relies on unlabeled documents and Wikipedia . a supervised approach uses only natural information, such as unlabed documents . |
| Approach: | They propose a method which exploits only naturally occurring information . they construct a high recall list of candidate entities for each mention in an unlabeled document . |
| Outcome: | The proposed model outperforms fully-supervised state-of-the-art systems on standard test sets. |
Fine-Grained Evaluation for Entity Linking (D19-1)
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| Challenge: | Entity Linking (EL) is an Information Extraction task that identifies entity mentions in a text corpus and associates them with an unambiguous identifier in KBs such as Wikipedia, BabelNet, DBpedia, Wikidata and YAGO. |
| Approach: | They propose a fine-grained categorization of different types of entity mentions and links and propose 'fuzzy recall' metric to address the lack of consensus and compare a selection of online EL systems. |
| Outcome: | The proposed task offers a bridge between unstructured text and structured KBs, where EL has applications for semantic search, document classification, relation extraction, and more. |
WEC: Deriving a Large-scale Cross-document Event Coreference dataset from Wikipedia (2021.naacl-main)
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| Challenge: | Existing datasets for cross-document event coreference resolution are limited and small . authors present a method for identifying clusters of text mentions that refer to the same event . |
| Approach: | They propose a method for generating a large-scale Wikipedia event coreference dataset . they use a generic approach that adapts state-of-the-art models to the cross-document setting . |
| Outcome: | The proposed method outperforms existing models and can be applied to other languages. |
WEXEA: Wikipedia EXhaustive Entity Annotation (2020.lrec-1)
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| Challenge: | Existing methods for extracting factual knowledge from text are limited to a few subtasks. |
| Approach: | They propose to use Wikipedia to build a corpus with exhaustive annotations of entity mentions. |
| Outcome: | The proposed system can be used to build supervised datasets and can be reproduced by everyone. |