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Fine-grained opinion analysis methods often make use of linguistic features but typically do not take the interaction between opinions into account. This article describes a set of experiments that demonstrate that relational features, mainly derived from dependency-syntactic and semantic role structures, can significantly improve the performance of automatic systems for a number of fine-grained opinion analysis tasks: marking up opinion expressions, finding opinion holders, and determining the polarities of opinion expressions. These features make it possible to model the way opinions expressed in natural-language discourse interact in a sentence over arbitrary distances. The use of relations requires us to consider multiple opinions simultaneously, which makes the search for the optimal analysis intractable. However, a reranker can be used as a sufficiently accurate and efficient approximation. A number of feature sets and machine learning approaches for the rerankers are evaluated. For the task of opinion expression extraction, the best model shows a 10-point absolute improvement in soft recall on the MPQA corpus over a conventional sequence labeler based on local contextual features, while precision decreases only slightly. Significant improvements are also seen for the extended tasks where holders and polarities are considered: 10 and 7 points in recall, respectively. In addition, the systems outperform previously published results for unlabeled (6 F-measure points) and polarity-labeled (10–15 points) opinion expression extraction. Finally, as an extrinsic evaluation, the extracted MPQA-style opinion expressions are used in practical opinion mining tasks. In all scenarios considered, the machine learning features derived from the opinion expressions lead to statistically significant improvements.

Recent work on Model Informed Drug Discovery and Development (MID3) has noted the need for clarity in model description used in quantitative disciplines such as pharmacology and statistics. 1-3 Currently, models are encoded in a variety of computer languages and are shared through publications that rarely include original code and generally lack reproducibility. The DDMoRe Model Description Language (MDL) has been developed primarily as a language standard to facilitate sharing knowledge and understanding of models.

Semantic similarity has typically been measured across items of approximately similar sizes. As a result, similarity measures have largely ignored the fact that different types of linguistic item can potentially have similar or even identical meanings, and therefore are designed to compare only one type of linguistic item. Furthermore, nearly all current similarity benchmarks within NLP contain pairs of approximately the same size, such as word or sentence pairs, preventing the evaluation of methods that are capable of comparing different sized items. To address this, we introduce a new semantic evaluation called cross-level semantic similarity (CLSS), which measures the degree to which the meaning of a larger linguistic item, such as a paragraph, is captured by a smaller item, such as a sentence. Our pilot CLSS task was presented as part of SemEval-2014, which attracted 19 teams who submitted 38 systems. CLSS data contains a rich mixture of pairs, spanning from paragraphs to word senses to fully evaluate similarity measures that are capable of comparing items of any type. Furthermore, data sources were drawn from diverse corpora beyond just newswire, including domain-specific texts and social media. We describe the annotation process and its challenges, including a comparison with crowdsourcing, and identify the factors that make the dataset a rigorous assessment of a method's quality. Furthermore, we examine in detail the systems participating in the SemEval task to identify the common factors associated with high performance and which aspects proved difficult to all systems. Our findings demonstrate that CLSS poses a significant challenge for similarity methods and provides clear directions for future work on universal similarity methods that can compare any pair of items.

In European and many African, Middle Eastern and southern Asian populations, lactase persistence (LP) is the most strongly selected monogenic trait to have evolved over the past 10,000 years1. Although the selection of LP and the consumption of prehistoric milk must be linked, considerable uncertainty remains concerning their spatiotemporal configuration and specific interactions2,3. Here we provide detailed distributions of milk exploitation across Europe over the past 9,000 years using around 7,000 pottery fat residues from more than 550 archaeological sites. European milk use was widespread from the Neolithic period onwards but varied spatially and temporally in intensity. Notably, LP selection varying with levels of prehistoric milk exploitation is no better at explaining LP allele frequency trajectories than uniform selection since the Neolithic period. In the UK Biobank4,5 cohort of 500,000 contemporary Europeans, LP genotype was only weakly associated with milk consumption and did not show consistent associations with improved fitness or health indicators. This suggests that other reasons for the beneficial effects of LP should be considered for its rapid frequency increase. We propose that lactase non-persistent individuals consumed milk when it became available but, under conditions of famine and/or increased pathogen exposure, this was disadvantageous, driving LP selection in prehistoric Europe. Comparison of model likelihoods indicates that population fluctuations, settlement density and wild animal exploitation—proxies for these drivers—provide better explanations of LP selection than the extent of milk exploitation. These findings offer new perspectives on prehistoric milk exploitation and LP evolution. 336-345 200

We wish to understand the processes underlying the ionization dynamics of N2 as experimentally induced and studied by recording the kinetic energy release (KER) in a XUV-pump/IR-probe setup. To this end a theoretical model was developed describing the ionization process using Dyson Orbitals and, subsequently, the dissociation process using a large set of diabatic potential energy surfaces (PES) on which to propagate. From said set of PES, a small subset is extracted allowing for the identification of one and two photon processes chiefly responsible for the experimentally observed features We acknowledge support from the European Research Council under ERC Grants No. 637756 STARLIGHT, No. 227355 ELYCHE, and No. 290853 XCHEM, from LASERLAB-EUROPE (Grant Agreement No. 284464, EC’s Seventh Framework Programme), from European COST Action CM1204 XLIC, the MICINN Project FIS2013-42002-R, the ERA-Chemistry Project PIM2010EEC-00751, the European Grant MC-ITN CORINF. Calculations were performed at the Centro de Computación of the Universidad Autónoma de Madrid (CC-UAM) and the Barcelona Supercomputing Center (BSC). G. S. acknowledges the Italian Ministry of Research Project FIRB No. RBID08CRXK. R. L. and M. H. acknowledge a Marie Curie International Research Staff Exchange Scheme Fellowship (Grant Agreement No. PIRSES-GA-2012-31754, EC’s Seventh Framework Programme) and the COST Action CM1405 MOLIM. C. L. acknowledges National Natural Science Foundation of China (Grants No. 11127901, No. 61221064, and No. 11404356), 973 Project (Grant No. 2011CB808103). We are very grateful to Alicia Palacios for fruitful discussions on the ionization model

The growing interest in the impact assessment of cultural heritage has generated a diversity of approaches often focusing mainly on the economic dimension. However, if we consider the cultural heritage as a common and relational good, any activity aimed at promoting its educational, social and economic values needs to produce benefi ts for the community in which the good is located. Therefore, we must necessarily resort to other concepts and tools for measuring the impact of valorisation activities of cultural heritage on community well-being. We must also consider the value for community members. This article illustrates a qualitative research carried out through a case study with the direct involvement of local stakeholders (multi-stakeholder approach). This introductory study is part of a wider research program that the author is currently developing. In accordance with the Quality of Life studies, the main result of the research is the defi nition of elements useful to the impact assessment produced by the valorisation of cultural heritage, in terms of well-being of local communities. Il crescente interesse nella valutazione di impatto del patrimonio culturale ha generato una diversità di approcci, spesso incentrati sulla dimensione economica. Se però si considera il patrimonio culturale come bene comune e relazionale, qualsiasi attività volta a promuovere il suo valore educativo, sociale ed economico deve essere in grado di produrre benefi ci per la comunità in cui il bene si trova. È necessario, dunque, ricorrere a concetti e strumenti in grado di misurare l’impatto delle attività di valorizzazione del patrimonio sul benessere della comunità locale, considerando anche il valore attribuitogli dalla stessa. Questo articolo illustra una ricerca qualitativa realizzata attraverso un caso di studio con il coinvolgimento diretto degli attori locali (approccio multi-stakeholder). Si tratta di uno studio introduttivo ad un percorso di ricerca più ampio e articolato che l’autrice sta sviluppando. In accordo con gli studi sulla Qualità della Vita, il principale risultato della ricerca è la defi nizione di elementi utili alla valutazione dell’impatto prodotto dalla valorizzazione del patrimonio culturale in termini di benessere della comunità locale. Il Capitale culturale. Studies on the value of cultural heritage, No 14 (2016): Musei e mostre tra le due Guerre