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This paper outlines procedures for enhancing WordNet with conceptual information from FrameNet. The mapping of the two resources is non-trivial. We define a number of techniques for the validation of the consistency of the mapping and the extension of its coverage which make use of the structure of both resources and the systematic relations between synsets in WordNet and between frames in FrameNet, as well as between synsets and frames). We present a case study on causativity, a relation which provides enhancement complementary to the one using hierarchical relations, by means of linking in a systematic way large parts of the lexicon. We show how consistency checks and denser relations may be implemented on the basis of this relation. We, then, propose new frames based on causative-inchoative correspondences and in conclusion touch on the possibilities for defining new frames based on the types of specialisation that takes place from parent to child synset.

This paper presents a modular modeling concept for urban areas, based on the high-level definition of elements, infrastructures and resources. The simplified models and the definition of components as interfaces among domains enable a multi-domain simulation for energy and non-energy systems. Domain, component and interface models are explained in detail. Modeling and simulation are open-source and implemented in Python to enable the integration of the concept into the simulation platform PyCity. The demonstration scenario includes the domains electricity, gas, heat, waste, water and waste water, and shows the benefit of the multi-domain simulation for the increase of efficiency in the system by local exploitation of synergies among domains. This work provides a basic concept which enables the integration of energy and non-energy domains in a comprehensive simulation framework.

International audience; Multiword expressions (MWEs) are known as a "pain in the neck" for NLP due to their idiosyncratic behaviour. While some categories of MWEs have been addressed by many studies, verbal MWEs (VMWEs), such as to take a decision, to break one's heart or to turn off, have been rarely modelled. This is notably due to their syntactic variability, which hinders treating them as " words with spaces ". We describe an initiative meant to bring about substantial progress in understanding, modelling and processing VMWEs. It is a joint effort, carried out within a European research network, to elaborate universal terminologies and annotation guidelines for 18 languages. Its main outcome is a multilingual 5-million-word annotated corpus which underlies a shared task on automatic identification of VMWEs. This paper presents the corpus annotation methodology and outcome, the shared task organisation and the results of the participating systems.

In this work, we introduce the short-term phase of a cooperative energy management algorithm, which exploits the flexibilities arising from the shifted switching of heating systems and charging and discharging of thermal storage. The cooperative approach does not follow any financial gain optimization for individual households but rather targets a solution that is beneficial for all parties involved. We introduce profile deviation categories based on previous studies and define the concept of flexibility provided by the heating systems according to operational boundaries and the capacity of the thermal storage. Within this short-term phase of the energy management algorithm, the switching of the heating systems is coordinated in order to avoid transient effects due to simultaneous switching. Furthermore, occurring deviations from a day-ahead schedule are detected, evaluated and analyzed, and an adequate combination of resources to dispatch is calculated in order to compensate the deviations. The method evaluates shortterm weather forecasts and user behavior forecasts, statistical data and signals from the upper grid level. The short-term load compensation is based on local state estimation and includes grid aspects. We present here preliminary results from a case study for a radial grid segment from the model region, in which the combination of dispatch resources is calculated such that their distance from the location of the primary cause of the deviation is minimized.

The paper discusses several key concepts related to the development of corpora and reconsiders them in light of recent developments in NLP. On the basis of an overview of present-day corpora, we conclude that the dominant practices of corpus design do not utilise adequately the technologies and, as a result, fail to meet the demands of corpus linguistics, computational lexicology and computational linguistics alike.We proceed to lay out a data-driven approach to corpus design, which integrates the best practices of traditional corpus linguistics with the potential of the latest technologies allowing fast collection, automatic metadata description and annotation of large amounts of data. Thus, the gist of the approach we propose is that corpus design should be centred on amassing large amounts of mono- and multilingual texts and on providing them with a detailed metadata description and high-quality multi-level annotation.We go on to illustrate this concept with a description of the compilation, structuring, documentation, and annotation of the Bulgarian National Corpus (BulNC). At present it consists of a Bulgarian part of 979.6 million words, constituting the corpus kernel, and 33 Bulgarian-X language corpora, totalling 972.3 million words, 1.95 billion words altogether. The BulNC is supplied with a comprehensive metadata description, which allows us to organise the texts according to different principles. The Bulgarian part of the BulNC is automatically processed (tokenised and sentence split) and annotated at several levels: morphosyntactic tagging, lemmatisation, word-sense annotation, annotation of noun phrases and named entities. Some levels of annotation are also applied to the Bulgarian-English parallel corpus with the prospect of expanding multilingual annotation both in terms of linguistic levels and the number of languages for which it is available. We conclude with a brief evaluation of the quality of the corpus and an outline of its applications in NLP and linguistic research.

In order to build reliable and robust structures for the future power supply, flexibilities of electrical generation, consumption and storage have to be increased. Within Dual Demand Side Management, the feasibility of a city quarter to manage locally its energy supply and to stabilize the regional grid is investigated. The concept focuses on the potential thermal storage systems provided by the existing building stock and on heat pumps as well as combined heat and power systems as generation and flexible storage units. Further, the physical building model and the distribution grid model will be coupled to a multi-energy simulation platform, to investigate the energy management options and the load shifting potential. In this paper we present the challenges met in process of system modeling. The chosen approaches are presented in two main sections, modeling and simulation requirements, together with discussed alternative solutions, drawbacks and limitations. The section Modeling deals with the user model and electricity demand forecasting, the building and electrical models. Main issues in the development of the building model concern the lack of available information of residents' behavior and building characteristics for its parameterization. Computational issues regarding the multi-energy simulation platform are discussed separately for the model coupling, the electrical and thermal simulation in the section Simulation Requirements.