Entity Identifier: A Natural Text Parsing-based Framework For Entity
  Relation Extraction
El Mehdi Chouham Jessica López Espejel Mahaman Sanoussi Yahaya Alassan Walid Dahhane El Hassane Ettifouri
The field of programming has a diversity of paradigms that are used according
to the working framework. While current neural code generation methods are able
to learn and generate code directly from text, we believe that this approach is
not optimal for certain code tasks, particularly the generation of classes in
an object-oriented project. Specifically, we use natural language processing
techniques to extract structured information from requirements descriptions, in
order to automate the generation of CRUD (Create, Read, Update, Delete) class
code. To facilitate this process, we introduce a pipeline for extracting entity
and relation information, as well as a representation called an "Entity Tree"
to model this information. We also create a dataset to evaluate the
effectiveness of our approach.