The modeling of psycholinguistic phenomena, such as word reading, with machine learning techniques requires the featurization of word stimuli into appropriate orthographic and phonological representations. Critically, the choice of features impacts the performance of machine learning algorithms, and can have important ramifications for the conclusions drawn from a model. As such, featurizing words with a variety of feature sets, without having to resort to using different tools is beneficial development. In this work, we present wordkit, a python package which allows users to switch between feature sets and featurizers with a uniform API, allowing for rapid prototyping. To the best of our knowledge, this is the first package which integrates a variety of orthographic and phonological featurizers in a single package. The package is fully compatible with scikit-learn, and hence can be integrated into other pipelines. Furthermore, the package is modular and extensible, allowing for the integration of a large variety of feature sets and featurizers. The package and documentation can be found at github.com/stephantul/wordkit
@InProceedings{TULKENS18.249, author = {Stephan Tulkens and Dominiek Sandra and Walter Daelemans}, title = "{WordKit: a Python Package for Orthographic and Phonological Featurization}", 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} }
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