We use a convolutional neural network to perform authorship identification on a very homogeneous dataset of scientific publications. In order to investigate the effect of domain biases, we obscure words below a certain frequency threshold, retaining only their POS-tags. This procedure improves test performance due to better generalization on unseen data. Using our method, we are able to predict the authors of scientific publications in the same discipline at levels well above chance.

No abstract

We introduce a method for error detection in automatically annotated text, aimed at supporting the creation of high-quality language resources at affordable cost. Our method combines an unsupervised generative model with human supervision from active learning. We test our approach on in-domain and out-of-domain data in two languages, in AL simulations and in a real world setting. For all settings, the results show that our method is able to detect annotation errors with high precision and high recall.

Active learning (AL) is a technique for reducing manual annotation effort during the annotation of training data for machine learning classifiers. For NLP tasks, pool-based and stream-based sampling techniques have been used to select new instances for AL while generating new, artificial instances via Membership Query Synthesis was, up to know, considered to be infeasible for NLP problems. We present the first successful attempt to use Membership Query Synthesis for generating AL queries for natural language processing, using Variational Autoencoders for query generation. We evaluate our approach in a text classification task and demonstrate that query synthesis shows competitive performance to pool-based AL strategies while substantially reducing annotation time.

We introduce the first German treebank for Twitter microtext, annotated within the framework of Universal Dependencies. The new treebank includes over 12,000 tokens from over 500 tweets, independently annotated by two human coders. In the paper, we describe the data selection and annotation process and present baseline parsing results for the new testsuite.