Traditional Machine Learning and Deep Learning-based Text Classification for Turkish Law Documents using Transformers and Domain Adaptation

Natural Language Processing (NLP) is an interdisciplinary field between linguistics and computer science. Its main aim is to process natural (human) language using computer programs. Text classification is one of the main tasks of this field, and they are widely used in many different applications such as spam filtering, sentiment analysis, and document categorization. Nonetheless, there is only very little text classification work in the law domain and even less for the Turkish language. This may be attributed to the complexity within the domain. The length, complexity of documents, and use of extensive technical jargon are some of the reasons that distinguish this domain from others. Similar to the medical domain, understanding these documents requires extensive specialization. Another reason can be the scarcity of publicly available datasets. In this study, we compile sizeable unsupervised and supervised datasets from publicly available sources and experiment with several classification algorithms ranging from traditional classifiers to much more complicated deep learning and transformer-based models along with different text representations. We focus on classifying Court of Cassation decisions for their crime labels. Interestingly, the majority of the models we experiment with could be able to obtain good results. This suggests that although understanding the documents in the legal domain is complicated and requires expertise from humans, it may be relatively easier for machine learning models despite the extensive presence of the technical terms. This seems to be especially the case for transformer-based pre-trained neural language models which can be adapted to the law domain, showing high potential for future real-world applications.