Maximilian Koppatz


Automatic Generation of Factual News Headlines in Finnish
Maximilian Koppatz | Khalid Alnajjar | Mika Hämäläinen | Thierry Poibeau
Proceedings of the 15th International Conference on Natural Language Generation


Projecting named entity recognizers without annotated or parallel corpora
Jue Hou | Maximilian Koppatz | José María Hoya Quecedo | Roman Yangarber
Proceedings of the 22nd Nordic Conference on Computational Linguistics

Named entity recognition (NER) is a well-researched task in the field of NLP, which typically requires large annotated corpora for training usable models. This is a problem for languages which lack large annotated corpora, such as Finnish. We propose an approach to create a named entity recognizer with no annotated or parallel documents, by leveraging strong NER models that exist for English. We automatically gather a large amount of chronologically matched data in two languages, then project named entity annotations from the English documents onto the Finnish ones, by resolving the matches with limited linguistic rules. We use this “artificially” annotated data to train a BiLSTM-CRF model. Our results show that this method can produce annotated instances with high precision, and the resulting model achieves state-of-the-art performance.