{
  "id": "cs/0703135",
  "version": "v1",
  "published": "2007-03-27T22:12:25.000Z",
  "updated": "2007-03-27T22:12:25.000Z",
  "title": "Dependency Parsing with Dynamic Bayesian Network",
  "authors": [
    "Virginia Savova",
    "Leonid Peshkin"
  ],
  "comment": "6 pages",
  "journal": "In proceedings of American Association for Artificial Intelligence AAAI 2005",
  "categories": [
    "cs.CL",
    "cs.AI"
  ],
  "abstract": "Exact parsing with finite state automata is deemed inappropriate because of the unbounded non-locality languages overwhelmingly exhibit. We propose a way to structure the parsing task in order to make it amenable to local classification methods. This allows us to build a Dynamic Bayesian Network which uncovers the syntactic dependency structure of English sentences. Experiments with the Wall Street Journal demonstrate that the model successfully learns from labeled data.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2007-03-27T22:12:25.000Z"
    }
  ],
  "analyses": {
    "subjects": [
      "I.2.7",
      "I.2.1",
      "G.3",
      "H.3.1"
    ],
    "keywords": [
      "dynamic bayesian network",
      "dependency parsing",
      "wall street journal demonstrate",
      "local classification methods",
      "finite state automata"
    ],
    "tags": [
      "journal article"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 6,
      "language": "en",
      "license": "arXiv",
      "status": "editable",
      "adsabs": "2007cs........3135S"
    }
  }
}