@article{20229,
  keywords = {data science, machine learning, NLP, semantic similarity search, text similarity, thesaurus, triples},
  author = {Giavid Valiyev and Marcello Piraino and Arvid Kok and Michael Street and Ivana Mestric and Retzius Birger},
  title = {Initial Exploitation of Natural Language Processing Techniques on NATO Strategy and Policies},
  abstract = {<p>This paper describes initial exploitation of Natural Language Processing (NLP) techniques applied to a specific set of related NATO documents. In particular, the text similarity technique was applied to document sets with the aim of capturing the relationships between documents or sections of documents from semantic and syntactic perspectives. Thesaurus and triple extraction techniques allowed the understanding of the sentences beyond the syntactic structure, thus improving the accuracy in capturing similar content across documents with diverse syntactic structures. The objective is to assess whether Natural Language Processing tools can retrieve relationships and gaps between such kinds of textual data. This work improves interoperability in NATO by enhancing the development and application of policies, directives and other documents, which dictate how Consultation, Command and Control (C3) systems across the Alliance interoperate and support NATO's operational needs.</p>},
  year = {2020},
  journal = {Information & Security: An International Journal},
  volume = {47},
  chapter = {187},
  pages = {187-202},
  month = {2020},
  doi = {https://doi.org/10.11610/isij.4713},
  language = {eng},
}