Event Extraction: A Survey

• URL: http://arxiv.org/abs/2210.03419v2
• Date: Mon, 10 Oct 2022 06:14:04 GMT
• Title: Event Extraction: A Survey
• Authors: Viet Dac Lai
• Abstract summary: Extracting the reported events from text is one of the key research themes in natural language processing. The applications of event extraction spans across a wide range of domains such as newswire, biomedical domain, history and humanity, and cyber security. This report presents a comprehensive survey for event detection from textual documents.
• Score: 3.3758186776249928
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Extracting the reported events from text is one of the key research themes in natural language processing. This process includes several tasks such as event detection, argument extraction, role labeling. As one of the most important topics in natural language processing and natural language understanding, the applications of event extraction spans across a wide range of domains such as newswire, biomedical domain, history and humanity, and cyber security. This report presents a comprehensive survey for event detection from textual documents. In this report, we provide the task definition, the evaluation method, as well as the benchmark datasets and a taxonomy of methodologies for event extraction. We also present our vision of future research direction in event detection.

Related papers

• Double Mixture: Towards Continual Event Detection from Speech [60.33088725100812]
  Speech event detection is crucial for multimedia retrieval, involving the tagging of both semantic and acoustic events. This paper tackles two primary challenges in speech event detection: the continual integration of new events without forgetting previous ones, and the disentanglement of semantic from acoustic events. We propose a novel method, 'Double Mixture,' which merges speech expertise with robust memory mechanisms to enhance adaptability and prevent forgetting.
  arXiv  Detail & Related papers  (2024-04-20T06:32:00Z)
• Visual Text Meets Low-level Vision: A Comprehensive Survey on Visual Text Processing [4.057550183467041]
  The field of visual text processing has experienced a surge in research, driven by the advent of fundamental generative models. We present a comprehensive, multi-perspective analysis of recent advancements in this field.
  arXiv  Detail & Related papers  (2024-02-05T15:13:20Z)
• Towards Event Extraction from Speech with Contextual Clues [61.164413398231254]
  We introduce the Speech Event Extraction (SpeechEE) task and construct three synthetic training sets and one human-spoken test set. Compared to event extraction from text, SpeechEE poses greater challenges mainly due to complex speech signals that are continuous and have no word boundaries. Our method brings significant improvements on all datasets, achieving a maximum F1 gain of 10.7%.
  arXiv  Detail & Related papers  (2024-01-27T11:07:19Z)
• Massively Multi-Lingual Event Understanding: Extraction, Visualization, and Search [2.633652471066059]
  ISI-Clear is a state-of-the-art, cross-lingual, zero-shot event extraction system. It makes global events available on-demand, processing user-supplied text in 100 languages.
  arXiv  Detail & Related papers  (2023-05-17T20:41:51Z)
• LED: A Dataset for Life Event Extraction from Dialogs [57.390999707053915]
  Lifelogging has gained more attention due to its wide applications, such as personalized recommendations or memory assistance. Life Event Dialog is a dataset containing fine-grained life event annotations on conversational data. We explore three information extraction (IE) frameworks to address the conversational life event extraction task.
  arXiv  Detail & Related papers  (2023-04-17T14:46:59Z)
• MUSIED: A Benchmark for Event Detection from Multi-Source Heterogeneous Informal Texts [7.43647091073357]
  Event detection (ED) identifies and classifies event triggers from unstructured texts. We propose a new large-scale Chinese event detection dataset based on user reviews, text conversations, and phone conversations.
  arXiv  Detail & Related papers  (2022-11-25T05:05:29Z)
• An overview of event extraction and its applications [1.8047694351309205]
  This study provides a comprehensive overview of the state-of-the-art event extraction methods and their applications from text. A trait of this survey is that it provides an overview in moderate complexity, avoiding involving too many details of particular approaches.
  arXiv  Detail & Related papers  (2021-11-05T01:37:47Z)
• COfEE: A Comprehensive Ontology for Event Extraction from text, with an online annotation tool [3.8995911009078816]
  Event Extraction (EE) seeks to derive information about specific incidents and their actors from the text. EE is useful in many domains such as building a knowledge base, information retrieval, summarization and online monitoring systems. COfEE consists of two hierarchy levels (event types and event sub-types) that include new categories relating to environmental issues, cyberspace, criminal activity and natural disasters.
  arXiv  Detail & Related papers  (2021-07-21T19:43:22Z)
• Positioning yourself in the maze of Neural Text Generation: A Task-Agnostic Survey [54.34370423151014]
  This paper surveys the components of modeling approaches relaying task impacts across various generation tasks such as storytelling, summarization, translation etc. We present an abstraction of the imperative techniques with respect to learning paradigms, pretraining, modeling approaches, decoding and the key challenges outstanding in the field in each of them.
  arXiv  Detail & Related papers  (2020-10-14T17:54:42Z)
• "What Are You Trying to Do?" Semantic Typing of Event Processes [94.3499255880101]
  This paper studies a new cognitively motivated semantic typing task, multi-axis event process typing. We develop a large dataset containing over 60k event processes, featuring ultra fine-grained typing on both the action and object type axes. We propose a hybrid learning framework, P2GT, which addresses the challenging typing problem with indirect supervision from glosses1and a joint learning-to-rank framework.
  arXiv  Detail & Related papers  (2020-10-13T22:37:29Z)
• 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.