PESE: Event Structure Extraction using Pointer Network based
Encoder-Decoder Architecture
- URL: http://arxiv.org/abs/2211.12157v1
- Date: Tue, 22 Nov 2022 10:36:56 GMT
- Title: PESE: Event Structure Extraction using Pointer Network based
Encoder-Decoder Architecture
- Authors: Alapan Kuila and Sudeshan Sarkar
- Abstract summary: Event extraction (EE) aims to find the events and event-related argument information from the text and represent them in a structured format.
In this paper, we represent each event record in a unique format that contains trigger phrase, trigger type, argument phrase, and corresponding role information.
Our proposed pointer network-based encoder-decoder model generates an event in each step by exploiting the interactions among event participants.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: The task of event extraction (EE) aims to find the events and event-related
argument information from the text and represent them in a structured format.
Most previous works try to solve the problem by separately identifying multiple
substructures and aggregating them to get the complete event structure. The
problem with the methods is that it fails to identify all the interdependencies
among the event participants (event-triggers, arguments, and roles). In this
paper, we represent each event record in a unique tuple format that contains
trigger phrase, trigger type, argument phrase, and corresponding role
information. Our proposed pointer network-based encoder-decoder model generates
an event tuple in each time step by exploiting the interactions among event
participants and presenting a truly end-to-end solution to the EE task. We
evaluate our model on the ACE2005 dataset, and experimental results demonstrate
the effectiveness of our model by achieving competitive performance compared to
the state-of-the-art methods.
Related papers
- Grounding Partially-Defined Events in Multimodal Data [61.0063273919745]
We introduce a multimodal formulation for partially-defined events and cast the extraction of these events as a three-stage span retrieval task.
We propose a benchmark for this task, MultiVENT-G, that consists of 14.5 hours of densely annotated current event videos and 1,168 text documents, containing 22.8K labeled event-centric entities.
Results illustrate the challenges that abstract event understanding poses and demonstrates promise in event-centric video-language systems.
arXiv Detail & Related papers (2024-10-07T17:59:48Z) - MAVEN-Arg: Completing the Puzzle of All-in-One Event Understanding Dataset with Event Argument Annotation [104.6065882758648]
MAVEN-Arg is the first all-in-one dataset supporting event detection, event argument extraction, and event relation extraction.
As an EAE benchmark, MAVEN-Arg offers three main advantages: (1) a comprehensive schema covering 162 event types and 612 argument roles, all with expert-written definitions and examples; (2) a large data scale, containing 98,591 events and 290,613 arguments obtained with laborious human annotation; and (3) the exhaustive annotation supporting all task variants of EAE.
arXiv Detail & Related papers (2023-11-15T16:52:14Z) - Type-aware Decoding via Explicitly Aggregating Event Information for
Document-level Event Extraction [11.432496741340334]
Document-level event extraction faces two main challenges: arguments-scattering and multi-event.
This paper proposes a novel-based Explicitly Aggregating(SEA) model to address these limitations.
SEA aggregates event information into event type and role representations, enabling the decoding of event records based on specific type-aware representations.
arXiv Detail & Related papers (2023-10-16T15:10:42Z) - Token-Event-Role Structure-based Multi-Channel Document-Level Event
Extraction [15.02043375212839]
This paper introduces a novel framework for document-level event extraction, incorporating a new data structure called token-event-role.
The proposed data structure enables our model to uncover the primary role of tokens in multiple events, facilitating a more comprehensive understanding of event relationships.
The results demonstrate that our approach outperforms the state-of-the-art method by 9.5 percentage points in terms of the F1 score.
arXiv Detail & Related papers (2023-06-30T15:22:57Z) - Joint Event Extraction via Structural Semantic Matching [12.248124072173935]
Event Extraction (EE) is one of the essential tasks in information extraction.
This paper encodes the semantic features of event types and makes structural matching with target text.
arXiv Detail & Related papers (2023-06-06T07:42:39Z) - 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) - Event Data Association via Robust Model Fitting for Event-based Object Tracking [66.05728523166755]
We propose a novel Event Data Association (called EDA) approach to explicitly address the event association and fusion problem.
The proposed EDA seeks for event trajectories that best fit the event data, in order to perform unifying data association and information fusion.
The experimental results show the effectiveness of EDA under challenging scenarios, such as high speed, motion blur, and high dynamic range conditions.
arXiv Detail & Related papers (2021-10-25T13:56:00Z) - 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) - Text2Event: Controllable Sequence-to-Structure Generation for End-to-end
Event Extraction [35.39643772926177]
Event extraction is challenging due to the complex structure of event records and the semantic gap between text and event.
Traditional methods usually extract event records by decomposing the complex structure prediction task into multiple subtasks.
We propose Text2Event, a sequence-to-structure generation paradigm that can directly extract events from the text in an end-to-end manner.
arXiv Detail & Related papers (2021-06-17T04:00:18Z) - Document-level Event Extraction with Efficient End-to-end Learning of
Cross-event Dependencies [37.96254956540803]
We propose an end-to-end model leveraging Deep Value Networks (DVN), a structured prediction algorithm, to efficiently capture cross-event dependencies for document-level event extraction.
Our approach achieves comparable performance to CRF-based models on ACE05, while enjoys significantly higher computational efficiency.
arXiv Detail & Related papers (2020-10-24T05:28:16Z) - Detecting Ongoing Events Using Contextual Word and Sentence Embeddings [110.83289076967895]
This paper introduces the Ongoing Event Detection (OED) task.
The goal is to detect ongoing event mentions only, as opposed to historical, future, hypothetical, or other forms or events that are neither fresh nor current.
Any application that needs to extract structured information about ongoing events from unstructured texts can take advantage of an OED system.
arXiv Detail & Related papers (2020-07-02T20:44:05Z)
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.