Home

Table of Contents

• Getting started
  • Home
  • Installation
  • Operation
  • Codifier
  • Architecture
  • Final Progress Report
  • Reading List
• Algorithms
  • Amendment Detection
  • Topic Modeling
  • Ranking
  • Evaluation and Metrics
• Datasets and Continuous Integration
  • Fetching Documents
  • Processing Documents
• Documentation
  • API Documentation
  • RESTful API
• Development
  • Testing
  • Licensing
  • Future Work & Contributing
  • Document Embeddings

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About the project

We live in a complex regulatory environment. As citizens, we obey government regulations from many authorities. As members of organized societies and groups, we must obey organizational policies and rules. As social beings, we are bound by conventions we make with others. As individuals, they are bound by personal rules of conduct. The full number and size of regulations can be really scary. We can agree on some general principles but, at the same time, we can disagree on how these principles apply to specific situations. In order to minimize such disagreements, regulators are often obliged to create numerous regulations or very large regulations to deal with special cases.

In the recent years plenty of attention has been gathering around analyzing public sector texts via text mining methods enabled by modern libraries, algorithms and practices and bought to to the forefront by open source projects such as textblob, spaCy, SciPy, Tensorflow and NLTK. These collaborative productive efforts seem to be a shift towards more efficient understanding of natural language by machines which can be used in conjunction with public documents in order to provide useful tools for legislators. This emerging sector is usually referred as "Computational Law".

This project, developed under the auspices the Google Summer of Code 2018 Program, carries out the extraction of Government Gazette (ΦΕΚ) texts from the National Printing House (ET), cross-links them with each other and, finally, identifies and applies the amendments to the legal text by providing automatic codification of the Greek legislation using methods and techniques of Natural Language Processing. This will allow the elimination of bureaucratic procedures and great time savings for lawyers looking for the most recent versions of statutes in legal databases. The detection of amendments is automated in order to amend the amendments to the laws merged into a common law, a procedure known as codification of the law. The new "merged" / modified / codified laws can show the current text of a law at every moment. This is something that is being traditionally done by hand and our aim was to automate it.

Finally, the laws are clustered into topics according to their content using a non-supervised machine learning model (Latent Dirichlet Allocation) to provide a more holistic representation of Greek legislation. Also, for easier indexing, PageRank was used and therefore the interconnections of the laws were positively taken into account, because the more references there is a legislative text than the other the more important it is characterized.

Through the analysis, categorization and codification of the GG documents, this project facilitates key elements of everyday life such as the elimination of bureaucracy and the efficient management of public documents to implement tangible solutions, which allows huge savings for lawyers and citizens.