Predicting the Level of Text Standardness in User-generated Content

Non-standard language as it appears in user-generated content has recently attracted much attention. This paper proposes that non-standardness comes in two basic varieties, technical and linguistic, and develops a machine-learning method to discriminate between standard and nonstandard texts in these two dimensions. We describe the manual annotation of a dataset of Slovene user-generated content and the features used to build our regression models. We evaluate and discuss the results, where the mean absolute error of the best performing method on a three-point scale is 0.38 for technical and 0.42 for linguistic standardness prediction. Even when using no language-dependent information sources, our predictor still outperforms an OOVratio baseline by a wide margin. In addition, we show that very little manually annotated training data is required to perform good prediction. Predicting standardness can help decide when to attempt to normalise the data to achieve better annotation results with standard tools, and provide linguists who are interested in nonstandard language with a simple way of selecting only such texts for their research.