Tagging Complex Non-Verbal German Chunks with Conditional Random Fields

We report on chunk tagging methods for German that recognize complex non-verbal phrases using structural chunk tags with Conditional Random Fields (CRFs). This state-of-the-art method for sequence classification achieves 93.5% accuracy on newspaper text. For the same task, a classical trigram tagger approach based on Hidden Markov Models reaches a baseline of 88.1%. CRFs allow for a clean and principled integration of linguistic knowledge such as part-of-speech tags, morphological constraints and lemmas. The structural chunk tags encode phrase structures up to a depth of 3 syntactic nodes. They include complex prenominal and postnominal modifiers that occur frequently in German noun phrases

Driving with Style: Inverse Reinforcement Learning in General-Purpose Planning for Automated Driving

Behavior and motion planning play an important role in automated driving. Traditionally, behavior planners instruct local motion planners with predefined behaviors. Due to the high scene complexity in urban environments, unpredictable situations may occur in which behavior planners fail to match predefined behavior templates. Recently, general-purpose planners have been introduced, combining behavior and local motion planning. These general-purpose planners allow behavior-aware motion planning given a single reward function. However, two challenges arise: First, this function has to map a complex feature space into rewards. Second, the reward function has to be manually tuned by an expert. Manually tuning this reward function becomes a tedious task. In this paper, we propose an approach that relies on human driving demonstrations to automatically tune reward functions. This study offers important insights into the driving style optimization of general-purpose planners with maximum entropy inverse reinforcement learning. We evaluate our approach based on the expected value difference between learned and demonstrated policies. Furthermore, we compare the similarity of human driven trajectories with optimal policies of our planner under learned and expert-tuned reward functions. Our experiments show that we are able to learn reward functions exceeding the level of manual expert tuning without prior domain knowledge.Comment: Appeared at IROS 2019. Accepted version. Added/updated footnote, minor correction in preliminarie

Towards Augmented MDM: Overview of Design and Function Areas – A Literature Review

Nowadays, the handling of data is of great importance for companies due to the increasing amount of data by digitalization. Time-consuming tasks in master data management (MDM) must be automated to provide data-driven business models with adequate data quality in real time and thus achieve higher data value. To increase the level of automation in companies, technologies as artificial intelligence are used and applied in information systems, including systems for MDM. The corresponding tasks can be summarized under the term augmented MDM. However, it is not entirely clear which of these processes can fall under the scope of augmented MDM. This paper presents a systematic literature review of 20 examined research articles published in four literature and conference databases to determine design areas and functions of augmented MDM. The findings are one design element “systems” with eleven functions and a proposed definition of terms related to augmented MDM

Joint Aspect and Polarity Classification for Aspect-based Sentiment Analysis with End-to-End Neural Networks

In this work, we propose a new model for aspect-based sentiment analysis. In contrast to previous approaches, we jointly model the detection of aspects and the classification of their polarity in an end-to-end trainable neural network. We conduct experiments with different neural architectures and word representations on the recent GermEval 2017 dataset. We were able to show considerable performance gains by using the joint modeling approach in all settings compared to pipeline approaches. The combination of a convolutional neural network and fasttext embeddings outperformed the best submission of the shared task in 2017, establishing a new state of the art.Comment: EMNLP 201

The African Peer Review Mechanism (APRM) as a Tool to Improve Governance? Experience in Ghana

"The quality of governance in Africa is particularly in discussion since the end of the Cold War. In 2002, African states created the African Peer Review Mechanism (APRM) with the declared goal to improve governance on the continent. Around half of Africa's states have agreed on conducting a self-assessment and discuss this with other heads of state and government. Ghana was the front runner in this process. As the first county to undergo an APRM, Ghana applied and also shaped the rules of the APRM. The aim of this study, which is the final report of a working group conducted in Ghana, is to look into the experience with the APRM in Ghana. Research was based on numerous interviews with stakeholders in the Ghanaian APRM, namely government officials, the national APRM secretariat, non-state actors and representatives of civil society. The study assesses the potential impact of the APRM on governance by considering the rigour or flexibility of its legal framework, the openness to participation in the self-assessment of Ghana, and the quality of the Ghanaian APRM report. It also takes a first look into whether recommendations of the report were implemented. From this analysis, the study assesses the APRM's potential for improving governance in Ghana and provides recommendations for APRM stakeholders and donors." (author's abstract

K-ion slides in Prussian blue analogues

We study the phenomenology of cooperative off-centering of K+ ions in potassiated Prussian blue analogues (PBAs). The principal distortion mechanism by which this off-centering occurs is termed a “K-ion slide”, and its origin is shown to lie in the interaction between local electrostatic dipoles that couple through a combination of electrostatics and elastic strain. Using synchrotron powder X-ray diffraction measurements, we determine the crystal structures of a range of low-vacancy K2M[Fe(CN)6] PBAs (M = Ni, Co, Fe, Mn, Cd) and establish an empirical link between composition, temperature, and slide-distortion magnitude. Our results reflect the common underlying physics responsible for K-ion slides and their evolution with temperature and composition. Monte Carlo simulations driven by a simple model of dipolar interactions and strain coupling reproduce the general features of the experimental phase behavior. We discuss the implications of our study for optimizing the performance of PBA K-ion battery cathode materials and also its relevance to distortions in other, conceptually related, hybrid perovskites