We describe the CoNLL-2003 shared task: language-independent named entity recognition.
We give background information on the data sets (English and German) and the evaluation …

Memory-based language processing (MBLP) views language processing as being based on
the direct reuse of previous experience rather than on the use of rules or other structures …

Machine learning based on various forms of inductive inference has been used for a wide
range of problems in natural language processing, with syntactic parsing being one of the …

Memory-Based Learning (MBL) is an elegantly simple and robust machine-learning method
applicable to a wide range of tasks in Natural Language Processing (NLP). Starting from our …

Interpretable rationales for model predictions play a critical role in practical applications. In
this study, we develop models possessing interpretable inference process for structured …

Named entity recognition and classification research has so far mainly focused on
supervised techniques and has typically considered only small sets of classes with regard to …

Questions are asked and answered every day. Question answering (QA) technology aims to
deliver the same facility online. It goes further than the more familiar search based on …

Interpretable rationales for model predictions are crucial in practical applications. We
develop neural models that possess an interpretable inference process for dependency …

The thesis describes an automatic anaphora resolution (AR) system for Norwegian,
focussing on the resolution of pronominal anaphora in fiction material. The system relies …

Most natural language processing (NLP) tasks require the translation of one level of
representation to another. For example, in text to speech systems, it is necessary to have a …