| Challenge: | Existing event extraction methods require predefined event types and their annotations to learn event extractors. |
| Approach: | They propose to represent each event type as a cluster of predicate sense, object head> pairs. |
| Outcome: | The proposed method can discover salient and high-quality event types on three datasets from different domains. |
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Semi-supervised New Event Type Induction and Event Detection (2020.emnlp-main)
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| Challenge: | Existing event extraction studies assume a set of event types and corresponding annotations are given, which could be expensive. |
| Approach: | They propose a semi-supervised task of event type induction to learn seen and unseen types . they use seen type annotations to optimize the process and enforce the reconstruction . |
| Outcome: | The proposed method achieves state-of-the-art on supervised event detection and discovers high-quality new types. |
Open Relation and Event Type Discovery with Type Abstraction (2022.emnlp-main)
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| Challenge: | Conventional "closed-world" information extraction methods rely on human ontologies to define scope for extraction. |
| Approach: | They propose a type abstraction approach where models are prompted to generalize and name the type . they use the similarity between inferred names to induce clusters . |
| Outcome: | The proposed method is complementary to token representations on relation extraction and event extraction datasets. |
Semi-supervised New Event Type Induction and Description via Contrastive Loss-Enforced Batch Attention (2023.eacl-main)
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| Challenge: | Existing methods for event extraction use annotated event types but are expensive and time-consuming. |
| Approach: | They propose a semi-supervised approach to learning new event types using a masked contrastive loss. |
| Outcome: | The proposed method learns similarities between clusters by enforcing an attention mechanism over the data minibatch. |
Open Domain Event Extraction Using Neural Latent Variable Models (P19-1)
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| Challenge: | Existing work on extracting events from news documents focuses on a set of pre-specified event types. |
| Approach: | They propose a latent variable neural model which is scalable to large corpus. |
| Outcome: | The proposed model performs better than the state-of-the-art method for event schema induction. |
Automatic Data Acquisition for Event Coreference Resolution (2021.eacl-main)
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| Challenge: | lexical paraphrases and high precision rules informed by news discourse structure can be used to collect coreferential and non-coreferential event pairs from unlabeled English news articles. |
| Approach: | They propose to use lexical paraphrases and news discourse structure to automatically collect coreferential and non-coreferential event pairs from unlabeled English news articles. |
| Outcome: | The proposed model performs better than the supervised model on evaluation datasets with different event domains and text genres. |
Query and Extract: Refining Event Extraction as Type-oriented Binary Decoding (2022.findings-acl)
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| Challenge: | Existing approaches to event extraction are limited to a set of pre-defined types. |
| Approach: | They propose a natural language query framework that uses event types and argument roles to extract candidate triggers and arguments from input text. |
| Outcome: | The proposed framework outperforms existing methods on zero-shot event extraction. |
CEO: Corpus-based Open-Domain Event Ontology Induction (2024.findings-eacl)
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| Challenge: | Existing event-centric NLP models restrict their generalization capabilities by limiting the pre-defined ontology. |
| Approach: | They propose a Corpus-based Event Ontology induction model to relax the restriction imposed by pre-defined ontologies. |
| Outcome: | The proposed model can induce a hierarchical event ontology with meaningful names on eleven open-domain corpora, making it more trustworthy and easier to be further curated. |
Rapid Customization for Event Extraction (P19-3)
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| Challenge: | a novel system allows users to customize event extraction to find new event types and their arguments. |
| Approach: | They propose a system that allows a user to find, expand and filter event triggers by exploring an unannotated development corpus. |
| Outcome: | The proposed system can find, expand and filter event triggers from an unannotated development corpus . it trains a generic argument attachment model for extracting Actor, Place, and Time . |
Extending Event Detection to New Types with Learning from Keywords (D19-55)
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| Challenge: | Existing methods for event detection classify words or phrases into specific types of interest. |
| Approach: | They propose a new event detection formulation that describes types via keywords to match contexts in documents. |
| Outcome: | The proposed formulation improves the performance of the proposed model to new types. |
Event-Centric Natural Language Processing (2021.acl-tutorials)
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| Challenge: | This tutorial will provide an introduction to various methods for automating the extraction, conceptualization and prediction of events and their relations. |
| Approach: | This tutorial will provide an introduction to various methods for automating events and their relations, and a wide range of NLU and commonsense understanding tasks. |
| Outcome: | This tutorial will provide an introduction to various methods for automating extraction, conceptualization and prediction of events and their relations, and a wide range of NLU and commonsense understanding tasks. |