uniblock: Scoring and Filtering Corpus with Unicode Block Information
This addresses a preprocessing bottleneck for NLP practitioners by automating character filtering, though it is incremental as it builds on existing statistical techniques.
The paper tackled the problem of tedious and repetitive rule-based removal of illegal characters in NLP preprocessing by introducing uniblock, a statistical method that uses Unicode block information to score and filter sentences, showing effectiveness in tasks like Sentiment Analysis, Language Modeling, and Machine Translation.
The preprocessing pipelines in Natural Language Processing usually involve a step of removing sentences consisted of illegal characters. The definition of illegal characters and the specific removal strategy depend on the task, language, domain, etc, which often lead to tiresome and repetitive scripting of rules. In this paper, we introduce a simple statistical method, uniblock, to overcome this problem. For each sentence, uniblock generates a fixed-size feature vector using Unicode block information of the characters. A Gaussian mixture model is then estimated on some clean corpus using variational inference. The learned model can then be used to score sentences and filter corpus. We present experimental results on Sentiment Analysis, Language Modeling and Machine Translation, and show the simplicity and effectiveness of our method.