NLPExplorer

A Multilingual Evaluation of Three Spelling Normalisation Methods for Historical Text

Eva Pettersson | Beáta Megyesi | Joakim Nivre |

Month: April
Year: 2014
Location: Gothenburg, Sweden
Venue: LaTeCH | WS |
SIG: SIGHUM

An Evaluation of Neural Machine Translation Models on Historical Spelling Normalization
Gongbo Tang | Fabienne Cap | Eva Pettersson | Joakim Nivre |

Combining Ontologies and Neural Networks for Analyzing Historical Language Varieties. A Case Study in Middle Low German
Maria Sukhareva | Christian Chiarcos |

The Uppsala Corpus of Student Writings: Corpus Creation, Annotation, and Analysis
Beáta Megyesi | Jesper Näsman | Anne Palmér |

Evaluating Historical Text Normalization Systems: How Well Do They Generalize?
Alexander Robertson | Sharon Goldwater |

Combining Phonology and Morphology for the Normalization of Historical Texts
Izaskun Etxeberria | Iñaki Alegria | Larraitz Uria | Mans Hulden |

Normalizing Medieval German Texts: from rules to deep learning
Natalia Korchagina |

Comparing Rule-based and SMT-based Spelling Normalisation for English Historical Texts
Gerold Schneider | Eva Pettersson | Michael Percillier |

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Tagging Machine Translation

Multilingual English

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Isomorphic Transfer of Syntactic Structures in Cross-Lingual NLP

Edoardo Maria Ponti | Roi Reichart | Anna Korhonen | Ivan Vulić |

One-Shot Neural Cross-Lingual Transfer for Paradigm Completion

Katharina Kann | Ryan Cotterell | Hinrich Schütze |

A Comparative Study of Minimally Supervised Morphological Segmentation