A Mixed Neural Network and Support Vector Machine Model for Tender Creation in the European Union TED Database

Sangramsing Kayte, Peter Schneider-Kamp


This research article proposes a new method of automatized text generation and subsequent classification of the European Union (EU) Tender Electronic Daily (TED) text documents into predefined technological categories of the dataset. The TED dataset provides information about the respective tenders includes features like Name of project, Title, Description, Types of contract, Common procurement vocabulary (CPV) code, and Additional CPV codes. The dataset is obtained from the SIMAP-Information system for the European public procurement website, which is comprised of tenders described in XML files. The dataset was preprocessed using tokenization, removal of stop words, removal of punctuation marks etc. We implemented a neural machine learning model based on Long Short-Term Memory (LSTM) nodes for text generation and subsequent code classification. Text generation means that given a single line or just two or three words of the title, the model generates the sequence of a whole sentence. After generating the title, the model predicts the main applicable CPV code for that title. The LSTM model reaches an accuracy of 97% for the text generation and 95% for code classification using Support Vector Machine(SVM). This experiment is a first step towards developing a system that based on TED data is able to auto-generate and code classify tender documents, easing the process of creating and disseminating tender information to TED and ultimately relevant vendors. The development and automation of this system will future vision and understand current undergoing projects and the deliveries by a SIMAP-Information system for European public procurement tenders organisation based on the tenders published by it.


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