A paper called “Classification of Intangible Social Innovation Concepts” that was submitted and accepted for presentation at 23rd International Conference on Natural Language & Information Systems (NLDB2018) and was held in Paris, France from 13th to 15th June 2018, received one of the best paper award. In total 3 papers were awarded as the best papers with no ranking or order between them. Papers also received monetary award.
NLDB is quite established (organised already for 23 years) and good conference in the area of natural language processing. Usually about 15-18% of papers submitted are accepted as long papers. It seems like some more papers are accepted as short papers and poster presentations, so the percentages of accepted papers is higher, but still it is probably bellow 50%. It is relatively hard to get in, which I manage to accomplish both times I submitted (2016 with paper called “Disentangling the Structure of Tables in Scientific Literature” and in 2018 with the “Classification of Intangible Social Innovation Concepts” ), but it is a real honor to be awarded the best paper award on such a venue.
Personally, there was many things going on when I was submitting the paper, from preparing for my Viva to working on the post-doc project, but I set the goal to visit Paris, and for a reason of the conference. I have never been in Paris and therefore this was a great opportunity to do so. I have to say that I am proud to manage to do so.
The published paper is about creating crawlers for getting the information about social innovation projects, then using this data and annotating a subset of it. Using the created annotations we created a set of machine learning classifiers for a certain given criteria for social innovation. It turned out that our classifiers are giving quite well results.
The abstract of the papers is:
In social sciences, similarly to other fields, there is exponential growth of literature and textual data that people are no more able to cope with in a systematic manner. In many areas there is a need to catalogue knowledge and phenomena in a certain area. However, social science concepts and phenomena are complex and in many cases there is a dispute in the field between conflicting definitions. In this paper we present a method that catalogues a complex and disputed concept of social innovation by applying text mining and machine learning techniques. Recognition of social innovations is performed by decomposing a definitions into several more specific criteria (social objectives, social actor interactions, outputs and inattentiveness). For each of these criteria, a machine learning-based classifier is created that checks whether certain text satisfies given criteria. The criteria can be successfully classified with an F1-score of 0.83–0.86. The presented method is flexible, since it allows combining criteria in a later stage in order to build and analyse the definition of choice.
The author version of the paper can be found on the following link: http://inspiratron.org/wp-content/uploads/2018/06/NLDB_paper_52.pdf