{
  "id": "1809.09078",
  "version": "v1",
  "published": "2018-09-24T17:46:27.000Z",
  "updated": "2018-09-24T17:46:27.000Z",
  "title": "Jointly Multiple Events Extraction via Attention-based Graph Information Aggregation",
  "authors": [
    "Xiao Liu",
    "Zhunchen Luo",
    "Heyan Huang"
  ],
  "comment": "accepted by EMNLP 2018",
  "categories": [
    "cs.CL"
  ],
  "abstract": "Event extraction is of practical utility in natural language processing. In the real world, it is a common phenomenon that multiple events existing in the same sentence, where extracting them are more difficult than extracting a single event. Previous works on modeling the associations between events by sequential modeling methods suffer a lot from the low efficiency in capturing very long-range dependencies. In this paper, we propose a novel Jointly Multiple Events Extraction (JMEE) framework to jointly extract multiple event triggers and arguments by introducing syntactic shortcut arcs to enhance information flow and attention-based graph convolution networks to model graph information. The experiment results demonstrate that our proposed framework achieves competitive results compared with state-of-the-art methods.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2018-09-24T17:46:27.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "jointly multiple events extraction",
      "attention-based graph information aggregation",
      "extract multiple event triggers",
      "achieves competitive results"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}