A case study on the use of machine learning techniques for supporting technology watch

Technology Watch human agents have to read many documents in order to manually categorize and dispatch them to the correct expert, that will later add valued information to each document. In this two step process, the first one, the categorization of documents, is time consuming and relies on the knowledge of a human categorizer agent. It does not add direct valued information to the process that will be provided in the second step, when the document is revised by the correct expert. This paper proposes Machine Learning tools and techniques to learn from the manually pre-categorized data to automatically classify new content. For this work a real industrial context was considered. Text from original documents, text from added value information and Semantic Annotations of those texts were used to generate different models, considering manually preestablished categories. Moreover, three algorithms from different approaches were used to generate the models. Finally, the results obtained were compared to select the best model in terms of accuracy and also on the reduction of the amount of document readings (human workload).