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Integrating distributional and lexical information for semantic classification of words using MRMF
(2016)
Semantic classification of words using distributional features is usually based on the semantic similarity of words. We show on two different datasets that a trained classifier using the distributional features directly gives better results. We use Support Vector Machines (SVM) and Multirelational Matrix Factorization (MRMF) to train classifiers. Both give similar results. However, MRMF, that was not used for semantic classification with distributional features before, can easily be extended with more matrices containing more information from different sources on the same problem. We demonstrate the effectiveness of the novel approach by including information from WordNet. Thus we show, that MRMF provides an interesting approach for building semantic classifiers that (1) gives better results than unsupervised approaches based on vector similarity, (2) gives similar results as other supervised methods and (3) can naturally be extended with other sources of information in order to improve the results.
The CogALex-V Shared Task provides two datasets that consists of pairs of words along with a classification of their semantic relation. The dataset for the first task distinguishes only between related and unrelated, while the second data set distinguishes several types of semantic relations. A number of recent papers propose to construct a feature vector that represents a pair of words by applying a pairwise simple operation to all elements of the feature vector. Subsequently, the pairs can be classified by training any classification algorithm on these vectors. In the present paper we apply this method to the provided datasets. We see that the results are not better than from the given simple baseline. We conclude that the results of the investigated method are strongly depended on the type of data to which it is applied.
In distributional semantics words are represented by aggregated context features. The similarity of words can be computed by comparing their feature vectors. Thus, we can predict whether two words are synonymous or similar with respect to some other semantic relation. We will show on six different datasets of pairs of similar and non-similar words that a supervised learning algorithm on feature vectors representing pairs of words outperforms cosine similarity between vectors representing single words. We compared different methods to construct a feature vector representing a pair of words. We show that simple methods like pairwise addition or multiplication give better results than a recently proposed method that combines different types of features. The semantic relation we consider is relatedness of terms in thesauri for intellectual document classification. Thus our findings can directly be applied for the maintenance and extension of such thesauri. To the best of our knowledge this relation was not considered before in the field of distributional semantics.
Research question: Rivalries in team sports are commonly conceptualized as a threat to the fans’ identity. Therefore, past research has mainly focused on the negative consequences. However, theoretical arguments and empirical evidence suggest that rivalry has both negative and positive effects on fans’ self-concept. This research develops and empirically tests a model which captures and integrates these dual effects of rivalry.
Research methods: Data were collected via an on-site survey at home games of eight German Bundesliga football teams (N = 571). Structural equation modeling provides strong support for the proposed model.
Results and findings: In line with previous research, the results show that rivalry threatens fans’ identity as reflected in lower public collective self-esteem in relation to supporters of the rival team. However, the results also show that there are crucial positive consequences, such as higher perceptions of public collective self-esteem in relation to supporters of non-rival opponents, perceived ingroup distinctiveness and ingroup cohesion. These positive effects are mediated through increases in disidentification with the rival and perceived reciprocity of rivalry.
Implications: We contribute to the literature by providing a more balanced view of one of team sports’ key phenomena. Our results indicate that the prevalent conceptualization of rivalry as an identity threat should be amended by the positive consequences. Our research also offers guidance for the promotion of rivalries, where the managerial focus should be on creating a perception that a rivalry is reciprocal.
According to the third-person effect or the influence of presumed media influence approach, the presumption that the media has strong effects on other people can affect individuals’ attitudes and behavior. For instance, if people believe in strong media influences on others, they are more likely to increase their communication activities or support demands for restrictions on media. A standardized online survey among German journalists (N = 960) revealed that the stronger the journalists perceive the political online influence on the public to be, the more frequently they contradict unwanted political views in their articles. Moreover, even journalists are more likely to approve of restrictions on the Internet’s political influence, the stronger they believe the effects of online media to be. The data reveal no connections between communication activities and demands for restrictions.
Smart Cities require reliable means for managing installations that offer essential services to the citizens. In this paper we focus on the problem of evacuation of smart buildings in case of emergencies. In particular, we present an abstract architecture for situation-aware evacuation guidance systems in smart buildings, describe its key modules in detail, and provide some concrete examples of its structure and dynamics.
Surface atomic relaxation and magnetism on hydrogen-adsorbed Fe(110) surfaces from first principles
(2016)
We have computed adsorption energies, vibrational frequencies, surface relaxation and buckling for hydrogen adsorbed on a body-centred-cubic Fe(110) surface as a function of the degree of H coverage. This adsorption system is important in a variety of technological processes such as the hydrogen embrittlement in ferritic steels, which motivated this work, and the Haber–Bosch process. We employed spin-polarised density functional theory to optimise geometries of a six-layer Fe slab, followed by frozen mode finite displacement phonon calculations to compute Fe–H vibrational frequencies. We have found that the quasi-threefold (3f) site is the most stable adsorption site, with adsorption energies of ∼3.0 eV/H for all coverages studied. The long-bridge (lb) site, which is close in energy to the 3f site, is actually a transition state leading to the stable 3f site. The calculated harmonic vibrational frequencies collectively span from 730 to 1220 cm−1, for a range of coverages. The increased first-to-second layer spacing in the presence of adsorbed hydrogen, and the pronounced buckling observed in the Fe surface layer, may facilitate the diffusion of hydrogen atoms into the bulk, and therefore impact the early stages of hydrogen embrittlement in steels.
Background: Physician-rating websites have become a popular tool to create more transparency about the quality of health care providers. So far, it remains unknown whether online-based rating websites have the potential to contribute to a better standard of care. Objective: Our goal was to examine which health care providers use online rating websites and for what purposes, and whether health care providers use online patient ratings to improve patient care. Methods: We conducted an online-based cross-sectional study by surveying 2360 physicians and other health care providers (September 2015). In addition to descriptive statistics, we performed multilevel logistic regression models to ascertain the effects of providers' demographics as well as report card-related variables on the likelihood that providers implement measures to improve patient care. Results: Overall, more than half of the responding providers surveyed (54.66%, 1290/2360) used online ratings to derive measures to improve patient care (implemented measures: mean 3.06, SD 2.29). Ophthalmologists (68%, 40/59) and gynecologists (65.4%, 123/188) were most likely to implement any measures. The most widely implemented quality measures were related to communication with patients (28.77%, 679/2360), the appointment scheduling process (23.60%, 557/2360), and office workflow (21.23%, 501/2360). Scaled-survey results had a greater impact on deriving measures than narrative comments. Multilevel logistic regression models revealed medical specialty, the frequency of report card use, and the appraisal of the trustworthiness of scaled-survey ratings to be significantly associated predictors for implementing measures to improve patient care because of online ratings. Conclusions: Our results suggest that online ratings displayed on physician-rating websites have an impact on patient care. Despite the limitations of our study and unintended consequences of physician-rating websites, they still may have the potential to improve patient care.
Background: Interprofessionalism, considered as collaboration between medical professionals, has gained prominence over recent decades and evidence for its impact has grown. The steadily increasing number of residents in nursing homes will challenge medical care and the interaction across professions, especially nurses and general practitioners (GPS). The nursing home visit, a key element of medical care, has been underrepresented in research. This study explores GP perspectives on interprofessional collaboration with a focus on their visits to nursing homes in order to understand their experiences and expectations. This research represents an aspect of the interprof study, which explores medical care needs as well as the perceived collaboration and communication by nursing home residents, their families, GPS and nurses. This paper focusses on GPS' views, investigating in particular their visits to nursing homes in order to understand their experiences. Methods: Open guideline-interviews covering interprofessional collaboration and the visit process were conducted with 30 GPS in three study centers and analyzed with grounded theory methodology. GPS were recruited via postal request and existing networks of the research partners. Results: Four different types of nursing home visits were found: visits on demand, periodical visits, nursing home rounds and ad-hoc-decision based visits. We identified the core category "productive performance" of home visits in nursing homes which stands for the balance of GPŚ individual efforts and rewards. GPS used different strategies to perform a productive home visit: preparing strategies, on-site strategies and investing strategies. Conclusion: We compiled a theory of GPS home visits in nursing homes in Germany. The findings will be useful for research, and scientific and management purposes to generate a deeper understanding of GP perspectives and thereby improve interprofessional collaboration to ensure a high quality of care.
Discovery and efficient reuse of technology pictures using Wikimedia infrastructures. A proposal
(2016)
Multimedia objects, especially images and figures, are essential for the visualization and interpretation of research findings. The distribution and reuse of these scientific objects is significantly improved under open access conditions, for instance in Wikipedia articles, in research literature, as well as in education and knowledge dissemination, where licensing of images often represents a serious barrier.
Whereas scientific publications are retrievable through library portals or other online search services due to standardized indices there is no targeted retrieval and access to the accompanying images and figures yet. Consequently there is a great demand to develop standardized indexing methods for these multimedia open access objects in order to improve the accessibility to this material.
With our proposal, we hope to serve a broad audience which looks up a scientific or technical term in a web search portal first. Until now, this audience has little chance to find an openly accessible and reusable image narrowly matching their search term on first try - frustratingly so, even if there is in fact such an image included in some open access article.