Universal dependencies (UD) is a framework for morphosyntactic annotation of human language, which to date has been used to create treebanks for more than 100 languages. In this article, we outline the linguistic theory of the UD framework, which draws on a long tradition of typologically oriented grammatical theories. Grammatical relations between words are centrally used to explain how predicate–argument structures are encoded morphosyntactically in different languages while morphological features and part-of-speech classes give the properties of words. We argue that this theory is a good basis for cross-linguistically consistent annotation of typologically diverse languages in a way that supports computational natural language understanding as well as broader linguistic studies.

In this paper we describe the TurkuNLP entry at the CoNLL 2018 Shared Task on Multilingual Parsing from Raw Text to Universal Dependencies. Compared to the last year, this year the shared task includes two new main metrics to measure the morphological tagging and lemmatization accuracies in addition to syntactic trees. Basing our motivation into these new metrics, we developed an end-to-end parsing pipeline especially focusing on developing a novel and state-of-the-art component for lemmatization. Our system reached the highest aggregate ranking on three main metrics out of 26 teams by achieving 1st place on metric involving lemmatization, and 2nd on both morphological tagging and parsing.

We present a system for automatically identifying a multitude of biomedical entities from the literature. This work is based on our previous efforts in the BioCreative VI: Interactive Bio-ID Assignment shared task in which our system demonstrated state-of-the-art performance with the highest achieved results in named entity recognition. In this paper we describe the original conditional random field-based system used in the shared task as well as experiments conducted since, including better hyperparameter tuning and character level modeling, which led to further performance improvements. For normalizing the mentions into unique identifiers we use fuzzy character n-gram matching. The normalization approach has also been improved with a better abbreviation resolution method and stricter guideline compliance resulting in vastly improved results for various entity types. All tools and models used for both named entity recognition and normalization are publicly available under open license.