This paper presents a system for author profiling (AP) modeling that reduces the complexity and time of building a sophisticated model for a number of different AP tasks. The system is implemented in a cloud-based visual programming platform ClowdFlows and is publicly available to a wider audience. In the platform, we also implemented our already existing state of the art gender prediction model and tested it on a number of cross-genre tasks. The results show that the implemented model, which was trained on tweets, achieves results comparable to state of the art models for cross-genre gender prediction. There is however a noticeable decrease in accuracy when the genre of a test set is different from the genre of the train set.
@InProceedings{MARTINC18.310, author = {Matej Martinc and Senja Pollak}, title = "{Reusable workflows for gender prediction}", booktitle = {Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)}, year = {2018}, month = {May 7-12, 2018}, address = {Miyazaki, Japan}, editor = {Nicoletta Calzolari (Conference chair) and Khalid Choukri and Christopher Cieri and Thierry Declerck and Sara Goggi and Koiti Hasida and Hitoshi Isahara and Bente Maegaard and Joseph Mariani and Hélène Mazo and Asuncion Moreno and Jan Odijk and Stelios Piperidis and Takenobu Tokunaga}, publisher = {European Language Resources Association (ELRA)}, isbn = {979-10-95546-00-9}, language = {english} }
Summary of the paper