Assigning a category to a given word (tagging) depends on the particular word and on the categories (tags) of neighboring words. A theory that is able to assign tags to a given text can naturally be viewed as a recursive logic program. This article describes how iterative induction, a technique that has been proven powerful in the synthesis of recursive logic programs, has been applied to the t...

This paper describes work on the grammatical tagging of a newly created Norwegian speech corpus: the first corpus of modern Norwegian speech. We use an iterative procedure to perform computer-aided manual tagging of a part of the corpus. This material is then used to train the final taggers, which are applied to the rest of the corpus. We experiment with taggers that are based on three differen...

Active learning (AL) uses a data selection algorithm to select useful training samples minimize annotation cost. This is now an essential tool for building low-resource syntactic analyzers such as part-of-speech (POS) taggers. Existing AL heuristics are generally designed on the principle of selecting uncertain yet representative instances, where annotating these instances may reduce large numb...

We present a detailed description of our submission to the EmpiriST shared task 2015 for tokenization and part-of-speech tagging of German social media text. As relatively little training data is provided, neither tokenization nor PoS tagging can be learned from the data alone. For tokenization, our system uses regular expressions for general cases and word lists for exceptions. For PoS tagging...

While part-of-speech (POS) tagging and dependency parsing are observed to be closely related, existing work on joint modeling with manually crafted feature templates suffers from the feature sparsity and incompleteness problems. In this paper, we propose an approach to joint POS tagging and dependency parsing using transitionbased neural networks. Three neural network based classifiers are desi...