Modular resource development and diagnostic evaluation framework for fast NLP system improvement

Natural Language Processing systems are large-scale softwares, whose development involves many man-years of work, in terms of both coding and resource development. Given a dictionary of 110k lemmas, a few hundred syntactic analysis rules, 20k ngrams matrices and other resources, what will be the impact on a syntactic analyzer of adding a new possible category to a given verb? What will be the consequences of a new syntactic rules addition? Any modification may imply, besides what was expected, unforeseeable side-effects and the complexity of the system makes it difficult to guess the overall impact of even small changes. We present here a framework designed to effectively and iteratively improve the accuracy of our linguistic analyzer LIMA by iterative refinements of its linguistic resources. These improvements are continuously assessed by evaluating the analyzer performance against a reference corpus. Our first results show that this framework is really helpful towards this goal

Experimental Investigation of a Capacity-Based Demand Response Mechanism for District-Scale Applications

District heating and cooling systems incorporating heat recovery and large-scale thermal storage dramatically reduce energy waste and greenhouse gas emissions. Electrifying district energy systems also has the effect of introducing city-scale controllable loads at the level of the electrical substation. Here we explore the opportunity for these systems to provide energy services to the grid through capacity-based demand response mechanisms. We present both a planning approach to estimate available demand-side capacity and a control framework to guide real-time scheduling when the program is active. These tools are used to assess the technical feasibility and the economic viability of participating in capacity-based demand response in the context of a real-world, megawatt-scale pilot during the summer of 2018 on the Stanford University campus

Revisiting knowledge-based Semantic Role Labeling

International audienceSemantic role labeling has seen tremendous progress in the last years, both for supervised and unsupervised approaches. The knowledge-based approaches have been neglected while they have shown to bring the best results to the related word sense disambiguation task. We contribute a simple knowledge-based system with an easy to reproduce specification. We also present a novel approach to handle the passive voice in the context of semantic role labeling that reduces the error rate in F1 by 15.7%, showing that significant improvements can be brought while retaining the key advantages of the approach: a simple approach which facilitates analysis of individual errors, does not need any hand-annotated corpora and which is not domain-specific

Empirical Exploration of Zone-by-zone Energy Flexibility: a Non-intrusive Load Disaggregation Approach for Commercial Buildings

Building energy flexibility has been increasingly demonstrated as a cost-effective solution to respond to the needs of energy networks, including electric grids and district cooling and heating systems, improving the integration of intermittent renewable energy sources. Adjusting zonal temperature set-points is one of the most promising measures to unlock the energy flexibility potential of central air conditioning systems in complex commercial buildings. However, most existing studies focused on quantifying the energy flexibility on the building level since only building-level energy consumption is normally metered in commercial buildings. This study aims to investigate the impacts of temperature set-point adjustment strategies on zone-level thermal and energy performance by developing a non-intrusive data-driven load disaggregation method (i.e., a virtual zonal power meter). Three university buildings in Northern California were selected to test the proposed load disaggregation method. We found that heterogeneities of energy use and energy flexibility existed across not only buildings but also air handling units (AHUs) and zones. Moreover, a small number of zones accounted for a large amount of energy use and energy flexibility; and the most energy-intensive zones are not necessarily the most energy-flexible zones. For the three tested buildings, the top 30% most energy-intensive zones accounted for around 60% of the total energy use; and the top 30% most energy-flexible zones provided around 80% of the total energy flexibility. The proposed method enables the electric grid or district energy system operators to regard the controlled energy-flexible entities as a fleet of AHUs or zones instead of a fleet of buildings and helps unlock the possibility for targeted demand flexibility strategies that balance zone-by-zone energy reduction with zone-by-zone costs to occupants.Comment: 33 pages, 18 figure

WoNeF : amélioration, extension et évaluation d'une traduction française automatique de WordNet

National audienceIdentifier les sens possibles des mots du vocabulaire est un problème difficile demandant un travail manuel très conséquent. Ce travail a été entrepris pour l'anglais : le résultat est la base de données lexicale WordNet, pour laquelle il n'existe encore que peu d'équivalents dans d'autres langues. Néanmoins, des traductions automatiques de WordNet vers de nombreuses langues cibles existent, notamment pour le français. JAWS est une telle traduction automatique utilisant des dictionnaires et un modèle de langage syntaxique. Nous améliorons cette traduction, la complétons avec les verbes et adjectifs de WordNet, et démontrons la validité de notre approche via une nouvelle évaluation manuelle. En plus de la version principale nommée WoNeF, nous produisons deux versions supplémentaires : une version à haute précision (93% de précision, jusqu'à 97% pour les noms), et une version à haute couverture contenant 109 447 paires (littéral, synset)

Optimization of electric vehicle charging in a fully (nearly) electric campus energy system

The goal of this work is to build a set of computational tools to aid decision making for the modelling and operations of integrated urban energy systems that actively interact with the power grid of the future. District heating and cooling networks incorporating heat recovery and large-scale thermal storage, such as the Stanford campus system, dramatically reduce energy waste and greenhouse gas emissions. They have historically played a small, but important role at a local level. Here we explore the potential for other co-benefits, including the provision of load following services to the electrical grid, carbon emissions reductions or demand charge management. We formulate and solve the problem of optimally scheduling daily operations for different energy assets under a demand-charge-based tariff, given available historical data. We also explore the interaction and interdependence of an electrified thermal energy network with actively managed power sources and sinks that concurrently draw from the same electrical distribution feeder. At Stanford University, large-scale electric vehicle charging, on-site photovoltaic generation and controllable building loads could each separately represent up to 5 MW, or 15% of the aggregate annual peak power consumption in the very near future. We cooptimize financial savings from peak power reductions and shifting consumption to lower price periods and assess the flexibility of both the different components and the integrated energy system as a whole. We find that thermal storage, especially complemented with electric vehicle charging, can play the role that is often proposed for electrochemical storage for demand charge management applications and quantitatively evaluate potential revenue generators for an integrated urban energy system. Although there is little value to smart charging strategies for low penetrations of electric vehicles, they are needed to avoid significant increases in costs once penetration reaches a certain threshold – in the Stanford case, 750-1,000 vehicles, or 25% of the vehicle commuter population

Semantic Similarity To Improve Question Understanding in a Virtual Patient

In medicine, a communicating virtual patient or doctor allows students to train in medical diagnosis and develop skills to conduct a medical consultation. In this paper, we describe a conversational virtual standardized patient system to allow medical students to simulate a diagnosis strategy of an abdominal surgical emergency. We exploited the semantic properties captured by distributed word representations to search for similar questions in the virtual patient dialogue system. We created two dialogue systems that were evaluated on datasets collected during tests with students. The first system based on hand-crafted rules obtains $92.29\%$ as $F1$-score on the studied clinical case while the second system that combines rules and semantic similarity achieves $94.88\%$. It represents an error reduction of $9.70\%$ as compared to the rules-only-based system

Adapting VerbNet to French using existing resources

International audienceVerbNet is an English lexical resource for verbs that has proven useful for English NLP due to its high coverage and coherent classification. Such a resource doesn’t exist for other languages, despite some (mostly automatic and unsupervised) attempts. We show how to semi-automatically adapt VerbNet using existing resources designed for different purposes. This study focuses on French and uses two French resources: a semantic lexicon (Les Verbes Français) and a syntactic lexicon (Lexique-Grammaire)