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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)
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)
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)
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)
For the slot filling task of TAC KBP 2010 we developed as a system a simple pipeline architecture whose main components are a two-stage retrieval module and a relation extraction module. We use word-cluster features in the system as a method of achieving generalization by exploiting raw text. In the relation extraction module we use distant supervision in(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)