March 23, 2021 Conference paper Open Access

Yitagesu, Sofonias; Zhang, Xiaowang; Feng, Zhiyong; Li, Xiaohong; Xing, Zhenchang

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{
  "inLanguage": {
    "alternateName": "eng", 
    "@type": "Language", 
    "name": "English"
  }, 
  "description": "<p>Abstract&mdash;In this paper, we study the problem of part-of-speech (POS) tagging for security vulnerability descriptions (SVD). In<br>\ncontrast to newswire articles, SVD often contains a high-level natural language description of the text composed of mixed<br>\nlanguage studded with codes, domain-specific jargon, vague language, and abbreviations. Moreover, training data dedicated<br>\nto security vulnerability research is not widely available. Existing neural network-based POS tagging has often relied on manually<br>\nannotated training data or applying natural language processing (NLP) techniques, suffering from two significant drawbacks. The<br>\nformer is extremely time-consuming and requires labor-intensive feature engineering and expertise. The latter is inadequate to<br>\nidentify linguistically-informed words specific to the SVD domain. In this paper, we propose an automatic approach to assign POS<br>\ntags to tokens in SVD. Our approach uses the character-level representation to automatically extract orthographic features and<br>\nunsupervised word embeddings to capture meaningful syntactic and semantic regularities from SVD. The character level representations are then concatenated with the word embedding as a combined feature, which is then learned and used to predict<br>\nthe POS tagging. To deal with the issue of the poor availability of annotated security vulnerability data, we implement a finetuning approach. Our approach provides public access to a POS annotated corpus of &sim;8M tokens, which serves as a training dataset in this domain. Our evaluation results show a significant improvement in accuracy (17.72%-28.22%) of POS tagging in SVD over the current approaches.</p>", 
  "license": "https://creativecommons.org/licenses/by/4.0/legalcode", 
  "creator": [
    {
      "affiliation": "Tianjin University, China", 
      "@type": "Person", 
      "name": "Yitagesu, Sofonias"
    }, 
    {
      "affiliation": "Tianjin University, China", 
      "@type": "Person", 
      "name": "Zhang, Xiaowang"
    }, 
    {
      "affiliation": "Tianjin University, China", 
      "@type": "Person", 
      "name": "Feng, Zhiyong"
    }, 
    {
      "affiliation": "Tianjin University, China", 
      "@type": "Person", 
      "name": "Li, Xiaohong"
    }, 
    {
      "affiliation": "Australian National University, Australia", 
      "@type": "Person", 
      "name": "Xing, Zhenchang"
    }
  ], 
  "headline": "Automatic Part-of-Speech Tagging for Security Vulnerability Descriptions", 
  "image": "https://zenodo.org/static/img/logos/zenodo-gradient-round.svg", 
  "datePublished": "2021-03-23", 
  "keywords": [
    "Fine-Tuning, Part-of-Speech tagging, Unsupervised word embedding, Security vulnerability descriptions"
  ], 
  "url": "https://zenodo.org/record/4632063", 
  "@type": "ScholarlyArticle", 
  "contributor": [], 
  "@context": "https://schema.org/", 
  "identifier": "https://doi.org/10.5281/zenodo.4632063", 
  "@id": "https://doi.org/10.5281/zenodo.4632063", 
  "workFeatured": {
    "alternateName": "MSR 2021", 
    "@type": "Event", 
    "name": "The 2021 IEEE/ACM 18th International Conference on Mining Software Repositories"
  }, 
  "name": "Automatic Part-of-Speech Tagging for Security Vulnerability Descriptions"
}