Memory-Based Shallow Parsing

We present memory-based learning approaches to shallow parsing and apply these to five tasks: base noun phrase identification, arbitrary base phrase recognition, clause detection, noun phrase parsing and full parsing. We use feature selection techniques and system combination methods for improving the performance of the memory-based learner. Our approach is evaluated on standard data sets and the results are compared with that of other systems. This reveals that our approach works well for base phrase identification while its application towards recognizing embedded structures leaves some room for improvement

Introduction to the CoNLL-2002 Shared Task: Language-Independent Named Entity Recognition

We describe the CoNLL-2002 shared task: language-independent named entity recognition. We give background information on the data sets and the evaluation method, present a general overview of the systems that have taken part in the task and discuss their performance.Comment: 4 page

Introduction to the CoNLL-2000 Shared Task: Chunking

We describe the CoNLL-2000 shared task: dividing text into syntactically related non-overlapping groups of words, so-called text chunking. We give background information on the data sets, present a general overview of the systems that have taken part in the shared task and briefly discuss their performance.Comment: 6 page

Introduction to the CoNLL-2001 Shared Task: Clause Identification

We describe the CoNLL-2001 shared task: dividing text into clauses. We give background information on the data sets, present a general overview of the systems that have taken part in the shared task and briefly discuss their performance

Introduction to the CoNLL-2003 Shared Task: Language-Independent Named Entity Recognition

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 method, present a general overview of the systems that have taken part in the task and discuss their performance

Towards Transparent Linguistic Analysis of Dutch Newspaper Article Genres using Machine Learning

Systematic study of genre in newspapers sheds light on the development of journalism discourse. The genre conventions that can be discerned in a newspaper text signal the underlying discursive norms and practices of journalism as a profession. Historical newspapers are increasingly becoming available thanks to digital newspaper archives (in the Netherlands available through Delpher.nl), providing the opportunity for large-scale empirical research. However, the digital archives do not contain fine-grained genre information that is required for this purpose. Therefore, we use machine learning to automatically assign genre labels to newspaper articles.Machine learning facilitates substantial improvements to the outcomes of existing research by providing increased amounts of enriched data. However, the decision-making process of the machine learning pipeline needs to be verified. Our previous findings (Bilgin et al., 2018) show that accuracy scores alone are not enough to assess the performance of these pipelines and that making an informed choice not only empowers optimal study of the historical development of genre, but also increases the trustworthiness of the results. This work shows that employing a transparent approach driven by model interpretability facilitates fair comparison as well as validation of the underlying decision-making criteria of the machine learning pipelines. The criteria are presented in the form of important features, creating insights on interactions between genre-related linguistic features and bag-of-words features.</p

Five-year safety and efficacy of leadless pacemakers in a Dutch cohort

BACKGROUND: Adequate real-world safety and efficacy of leadless pacemakers (LPs) have been demonstrated up to 3 years after implantation. Longer-term data are warranted to assess the net clinical benefit of leadless pacing.OBJECTIVE: The purpose of this study was to evaluate the long-term safety and efficacy of LP therapy in a real-world cohort.METHODS: In this retrospective cohort study, all consecutive patients with a first LP implantation from December 21, 2012, to December 13, 2016, in 6 Dutch high-volume centers were included. The primary safety endpoint was the rate of major procedure- or device-related complications (ie, requiring surgery) at 5-year follow-up. Analyses were performed with and without Nanostim battery advisory-related complications. The primary efficacy endpoint was the percentage of patients with a pacing capture threshold ≤2.0 V at implantation and without ≥1.5-V increase at the last follow-up visit.RESULTS: A total of 179 patients were included (mean age 79 ± 9 years), 93 (52%) with a Nanostim and 86 (48%) with a Micra VR LP. Mean follow-up duration was 44 ± 26 months. Forty-one major complications occurred, of which 7 were not advisory related. The 5-year major complication rate was 4% without advisory-related complications and 27% including advisory-related complications. No advisory-related major complications occurred a median 10 days (range 0-88 days) postimplantation. The pacing capture threshold was low in 163 of 167 patients (98%) and stable in 157 of 160 (98%).CONCLUSION: The long-term major complication rate without advisory-related complications was low with LPs. No complications occurred after the acute phase and no infections occurred, which may be a specific benefit of LPs. The performance was adequate with a stable pacing capture threshold.</p