{
  "id": "1212.2504",
  "version": "v1",
  "published": "2012-10-19T15:06:52.000Z",
  "updated": "2012-10-19T15:06:52.000Z",
  "title": "Efficiently Inducing Features of Conditional Random Fields",
  "authors": [
    "Andrew McCallum"
  ],
  "comment": "Appears in Proceedings of the Nineteenth Conference on Uncertainty in Artificial Intelligence (UAI2003)",
  "categories": [
    "cs.LG",
    "stat.ML"
  ],
  "abstract": "Conditional Random Fields (CRFs) are undirected graphical models, a special case of which correspond to conditionally-trained finite state machines. A key advantage of these models is their great flexibility to include a wide array of overlapping, multi-granularity, non-independent features of the input. In face of this freedom, an important question that remains is, what features should be used? This paper presents a feature induction method for CRFs. Founded on the principle of constructing only those feature conjunctions that significantly increase log-likelihood, the approach is based on that of Della Pietra et al [1997], but altered to work with conditional rather than joint probabilities, and with additional modifications for providing tractability specifically for a sequence model. In comparison with traditional approaches, automated feature induction offers both improved accuracy and more than an order of magnitude reduction in feature count; it enables the use of richer, higher-order Markov models, and offers more freedom to liberally guess about which atomic features may be relevant to a task. The induction method applies to linear-chain CRFs, as well as to more arbitrary CRF structures, also known as Relational Markov Networks [Taskar & Koller, 2002]. We present experimental results on a named entity extraction task.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2012-10-19T15:06:52.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "conditional random fields",
      "efficiently inducing features",
      "arbitrary crf structures",
      "induction method applies",
      "higher-order markov models"
    ],
    "tags": [
      "conference paper"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable",
      "adsabs": "2012arXiv1212.2504M"
    }
  }
}