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Morfette is a modular, data-driven, probabilistic system which learns to perform joint morphological tagging and lemmatization from morphologically annotated corpora. The system is composed of two learning modules which are trained to predict morphological tags and lemmas using the Maximum Entropy classifier. The third module dynamically combines the(More)
Tweets often contain a large proportion of abbreviations, alternative spellings, novel words and other non-canonical language. These features are problematic for standard language analysis tools and it can be desirable to convert them to canoni-cal form. We propose a novel text nor-malization model based on learning edit operations from labeled data while(More)
Lemmatization for languages with rich inflectional morphology is one of the basic, indispensable steps in a language processing pipeline. In this paper we present a simple data-driven context-sensitive approach to lemmatizating word forms in running text. We treat lemmatization as a classification task for Machine Learning, and automatically induce class(More)
Named Entity Recognition is a relatively well-understood NLP task, with many publicly available training resources and software for English. Other languages tend to be underserved in this area. For German, CoNLL-2003 provides training data, but there are no publicly available, ready-to-use tools. We fill this gap and develop a German NER system with(More)
We present RelationFactory, a highly effective open source relation extraction system based on shallow modeling techniques. RelationFactory emphasizes mod-ularity, is easily configurable and uses a transparent pipelined approach. The interactive demo allows the user to pose queries for which RelationFactory retrieves and analyses contexts that contain(More)
Children learn a robust representation of lexical categories at a young age. We propose an incremental model of this process which efficiently groups words into lexical categories based on their local context using an information-theoretic criterion. We train our model on a corpus of child-directed speech from CHILDES and show that the model learns a(More)
Tokenization is widely regarded as a solved problem due to the high accuracy that rule-based tokenizers achieve. But rule-based tokenizers are hard to maintain and their rules language specific. We show that high-accuracy word and sentence segmentation can be achieved by using supervised sequence labeling on the character level combined with unsupervised(More)