Lemmatization, finding the basic morphological form of a word in a corpus, is an important step in many natural language processing tasks when working with morphologically rich languages. We describe and evaluate Nefnir, a new open source lemmatizer for Icelandic. Nefnir uses suffix substitution rules, derived from a large morphological database, to lemmatize tagged text. Evaluation shows that for correctly tagged text, Nefnir obtains an accuracy of 99.55%, and for text tagged with a PoS tagger, the accuracy obtained is 96.88%.

We report on work in progress which consists of annotating an Icelandic corpus for named entities (NEs) and using it for training a named entity recognizer based on a Bidirectional Long Short-Term Memory model. Currently, we have annotated 7,538 NEs appearing in the first 200,000 tokens of a 1 million token corpus, MIM-GOLD, originally developed for serving as a gold standard for part-of-speech tagging. Our best performing model, trained on this subset of MIM-GOLD, and enriched with external word embeddings, obtains an overall F1 score of 81.3% when categorizing NEs into the following four categories: persons, locations, organizations and miscellaneous. Our preliminary results are promising, especially given the fact that 80% of MIM-GOLD has not yet been used for training.