Corpus-based Open-Domain Event Type Induction

• URL: http://arxiv.org/abs/2109.03322v1
• Date: Tue, 7 Sep 2021 20:42:44 GMT
• Title: Corpus-based Open-Domain Event Type Induction
• Authors: Jiaming Shen, Yunyi Zhang, Heng Ji, Jiawei Han
• Abstract summary: This work presents a corpus-based open-domain event type induction method. We represent each event type as a cluster of predicate sense, object head> pairs. Our experiments, on three datasets from different domains, show our method can discover salient and high-quality event types.
• Score: 78.76531329136708
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Traditional event extraction methods require predefined event types and their corresponding annotations to learn event extractors. These prerequisites are often hard to be satisfied in real-world applications. This work presents a corpus-based open-domain event type induction method that automatically discovers a set of event types from a given corpus. As events of the same type could be expressed in multiple ways, we propose to represent each event type as a cluster of <predicate sense, object head> pairs. Specifically, our method (1) selects salient predicates and object heads, (2) disambiguates predicate senses using only a verb sense dictionary, and (3) obtains event types by jointly embedding and clustering <predicate sense, object head> pairs in a latent spherical space. Our experiments, on three datasets from different domains, show our method can discover salient and high-quality event types, according to both automatic and human evaluations.

Related papers

• Improving Event Definition Following For Zero-Shot Event Detection [66.27883872707523]
  Existing approaches on zero-shot event detection usually train models on datasets annotated with known event types. We aim to improve zero-shot event detection by training models to better follow event definitions.
  arXiv  Detail & Related papers  (2024-03-05T01:46:50Z)
• Zero- and Few-Shot Event Detection via Prompt-Based Meta Learning [45.3385722995475]
  We propose MetaEvent, a meta learning-based framework for zero- and few-shot event detection. In our framework, we propose to use the cloze-based prompt and a trigger-aware softr to efficiently project output to unseen event types. As such, the proposed MetaEvent can perform zero-shot event detection by mapping features to event types without any prior knowledge.
  arXiv  Detail & Related papers  (2023-05-27T05:36:46Z)
• Rich Event Modeling for Script Event Prediction [60.67635412135682]
  We propose the Rich Event Prediction (REP) framework for script event prediction. REP contains an event extractor to extract such information from texts. The core component of the predictor is a transformer-based event encoder to flexibly deal with an arbitrary number of arguments.
  arXiv  Detail & Related papers  (2022-12-16T05:17:59Z)
• Semantic Pivoting Model for Effective Event Detection [19.205550116466604]
  Event Detection aims to identify and classify mentions of event instances from unstructured articles. Existing techniques for event detection only use homogeneous one-hot vectors to represent the event type classes, ignoring the fact that the semantic meaning of the types is important to the task. We propose a Semantic Pivoting Model for Effective Event Detection (SPEED), which explicitly incorporates prior information during training and captures semantically meaningful correlations between input and events.
  arXiv  Detail & Related papers  (2022-11-01T19:20:34Z)
• PILED: An Identify-and-Localize Framework for Few-Shot Event Detection [79.66042333016478]
  In our study, we employ cloze prompts to elicit event-related knowledge from pretrained language models. We minimize the number of type-specific parameters, enabling our model to quickly adapt to event detection tasks for new types.
  arXiv  Detail & Related papers  (2022-02-15T18:01:39Z)
• Query and Extract: Refining Event Extraction as Type-oriented Binary Decoding [51.57864297948228]
  We propose a novel event extraction framework that takes event types and argument roles as natural language queries. Our framework benefits from the attention mechanisms to better capture the semantic correlation between the event types or argument roles and the input text.
  arXiv  Detail & Related papers  (2021-10-14T15:49:40Z)
• Unsupervised Label-aware Event Trigger and Argument Classification [73.86358632937372]
  We propose an unsupervised event extraction pipeline, which first identifies events with available tools (e.g., SRL) and then automatically maps them to pre-defined event types. We leverage pre-trained language models to contextually represent pre-defined types for both event triggers and arguments. We successfully map 83% of the triggers and 54% of the arguments to the correct types, almost doubling the performance of previous zero-shot approaches.
  arXiv  Detail & Related papers  (2020-12-30T17:47:24Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.