EXCEEDS: Extracting Complex Events as Connecting the Dots to Graphs in Scientific Domain

• URL: http://arxiv.org/abs/2406.14075v1
• Date: Thu, 20 Jun 2024 07:50:37 GMT
• Title: EXCEEDS: Extracting Complex Events as Connecting the Dots to Graphs in Scientific Domain
• Authors: Yi-Fan Lu, Xian-Ling Mao, Bo Wang, Xiao Liu, Heyan Huang,
• Abstract summary: We construct SciEvents, a large-scale multi-event document-level dataset with a schema tailored for scientific domain. Then, we propose EXCEEDS, a novel end-to-end scientific event extraction framework by storing dense nuggets in a grid matrix. Experimental results demonstrate state-of-the-art performances of EXCEEDS on SciEvents.
• Score: 57.56639626657212
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: It is crucial to utilize events to understand a specific domain. There are lots of research on event extraction in many domains such as news, finance and biology domain. However, scientific domain still lacks event extraction research, including comprehensive datasets and corresponding methods. Compared to other domains, scientific domain presents two characteristics: denser nuggets and more complex events. To solve the above problem, considering these two characteristics, we first construct SciEvents, a large-scale multi-event document-level dataset with a schema tailored for scientific domain. It has 2,508 documents and 24,381 events under refined annotation and quality control. Then, we propose EXCEEDS, a novel end-to-end scientific event extraction framework by storing dense nuggets in a grid matrix and simplifying complex event extraction into a dot construction and connection task. Experimental results demonstrate state-of-the-art performances of EXCEEDS on SciEvents. Additionally, we release SciEvents and EXCEEDS on GitHub.

Related papers

• CMNEE: A Large-Scale Document-Level Event Extraction Dataset based on Open-Source Chinese Military News [4.8309547228489125]
  We propose CMNEE, a large-scale, document-level open-source Chinese Military News Event Extraction dataset. It contains 17,000 documents and 29,223 events, which are all manually annotated based on a pre-defined schema for the military domain. We reproduce several state-of-the-art event extraction models with a systematic evaluation.
  arXiv  Detail & Related papers  (2024-04-18T15:02:35Z)
• 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)
• 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)
• MuLMS: A Multi-Layer Annotated Text Corpus for Information Extraction in the Materials Science Domain [0.7947524927438001]
  We present MuLMS, a new dataset of 50 open-access articles, spanning seven sub-domains of materials science. We present competitive neural models for all tasks and demonstrate that multi-task training with existing related resources leads to benefits.
  arXiv  Detail & Related papers  (2023-10-24T07:23:46Z)
• GET: Group Event Transformer for Event-Based Vision [82.312736707534]
  Event cameras are a type of novel neuromorphic sen-sor that has been gaining increasing attention. We propose a novel Group-based vision Transformer backbone for Event-based vision, called Group Event Transformer (GET) GET de-couples temporal-polarity information from spatial infor-mation throughout the feature extraction process.
  arXiv  Detail & Related papers  (2023-10-04T08:02:33Z)
• GLEN: General-Purpose Event Detection for Thousands of Types [80.99866527772512]
  We build a general-purpose event detection dataset GLEN, which covers 205K event mentions with 3,465 different types. GLEN is 20x larger in ontology than today's largest event dataset. We also propose a new multi-stage event detection model CEDAR specifically designed to handle the large size in GLEN.
  arXiv  Detail & Related papers  (2023-03-16T05:36:38Z)
• 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)
• Title2Event: Benchmarking Open Event Extraction with a Large-scale Chinese Title Dataset [19.634367718707857]
  We present Title2Event, a large-scale sentence-level dataset benchmarking Open Event Extraction without restricting event types. Title2Event contains more than 42,000 news titles in 34 topics collected from Chinese web pages. To the best of our knowledge, it is currently the largest manually-annotated Chinese dataset for open event extraction.
  arXiv  Detail & Related papers  (2022-11-02T04:39:36Z)
• EventGraph: Event Extraction as Semantic Graph Parsing [5.21480688623047]
  Event extraction involves the detection and extraction of both the event triggers and corresponding event arguments. We propose EventGraph, a joint framework for event extraction, which encodes events as graphs. Our code and models are released as open-source.
  arXiv  Detail & Related papers  (2022-10-16T22:11:46Z)
• 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)

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.