Detecting Locations from Twitter Messages Invited Talk

Abstract There is a large amount of information that can be extracted automatically from social media messages. Of particular interest are the topics discussed by the users, the opinions and emotions expressed, and the events and the locations mentioned. This work focuses on machine learning methods for detecting locations from Twitter messages, because the extracted locations can be useful in business, marketing and defence applications . There are two types of locations that we are interested in: location entities mentioned in the text of each message and the physical locations of the users. For the first type of locations (task 1), we detected expressions that denote locations and we classified them into names of cities, provinces/states, and countries. We approached the task in a novel way, consisting in two stages. In the first stage, we trained Conditional Random Field models with various sets of features. We collected and annotated our own dataset for training and testing. In the second stage, we resolved cases when more than one place with the same name exists, by applying a set of heuristics . For the second type of locations (task 2), we put together all the tweets written by a user, in order to predict his/her physical location. Only a few users declare their locations in their Twitter profiles, but this is sufficient to automatically produce training and test data for our classifiers. We experimented with two existing datasets collected from users located in the U.S. We propose a deep learning architecture for the solving the task, because deep learning was shown to work well for other natural language processing tasks, and because standard classifiers were already tested for the user location task. We designed a model that predicts the U.S. region of the user and his/her U.S. state, and another model that predicts the longitude and latitude of the user's location. We found that stacked denoising autoencoders are well suited for this task, with results comparable to the state-of-the-art

Maternal High Fat Diet Is Associated with Decreased Plasma n–3 Fatty Acids and Fetal Hepatic Apoptosis in Nonhuman Primates

To begin to understand the contributions of maternal obesity and over-nutrition to human development and the early origins of obesity, we utilized a non-human primate model to investigate the effects of maternal high-fat feeding and obesity on breast milk, maternal and fetal plasma fatty acid composition and fetal hepatic development. While the high-fat diet (HFD) contained equivalent levels of n-3 fatty acids (FA's) and higher levels of n-6 FA's than the control diet (CTR), we found significant decreases in docosahexaenoic acid (DHA) and total n-3 FA's in HFD maternal and fetal plasma. Furthermore, the HFD fetal plasma n-6∶n-3 ratio was elevated and was significantly correlated to the maternal plasma n-6∶n-3 ratio and maternal hyperinsulinemia. Hepatic apoptosis was also increased in the HFD fetal liver. Switching HFD females to a CTR diet during a subsequent pregnancy normalized fetal DHA, n-3 FA's and fetal hepatic apoptosis to CTR levels. Breast milk from HFD dams contained lower levels of eicosopentanoic acid (EPA) and DHA and lower levels of total protein than CTR breast milk. This study links chronic maternal consumption of a HFD with fetal hepatic apoptosis and suggests that a potentially pathological maternal fatty acid milieu is replicated in the developing fetal circulation in the nonhuman primate

Managing Knowledge for Asset Management: Shifting from Process to Relational Frames

The purpose of this paper is to review existing knowledge management (KM) practices within the field of asset management, identify gaps, and propose a new approach to managing knowledge for asset management. Existing approaches to KM in the field of asset management are incomplete with the focus primarily on the application of data and information systems, for example the use of an asset register. It is contended these approaches provide access to explicit knowledge and overlook the importance of tacit knowledge acquisition, sharing, and application. In doing so, current KM approaches within asset management tend to neglect the significance of relational factors; whereas studies in the KM field have showed that relational modes such as social capital is imperative for effective KM outcomes. In this paper, we argue that incorporating a relational approach to KM is more likely to contribute to the exchange of ideas and the development of creative responses necessary to improve decision making in asset management. This conceptual paper uses extant literature to explain KM antecedents and explore its outcomes in the context of asset management. KM is a component in the new integrated strategic asset management (ISAM) framework developed in conjunction with asset management industry associations (AAMCoG 2012) that improves asset management performance. In this paper, we use Nahapiet and Ghoshal’s [24] model to explain antecedents of relational approach to KM. Further, we develop an argument that relational KM is likely to contribute to the improvement of the ISAM framework components, such as organizational strategic management, service planning, and delivery. The main contribution of the paper is a novel and robust approach to managing knowledge that leads to the improvement of asset management outcomes

Dual paths to performance: the impact of global pressures on MNC subsidiary conduct and performance

Over the last decade, the international business literature has placed ever-greater emphasis on the role that learning and innovation play in determining multinational and multinational subsidiary performance. The present research seeks to understand the organizational paths leading to such desirable outcomes as greater learning, increased innovation and improved performance. Using a model tested with data collected through a survey of managers in subsidiaries of multinational firms, we find dual, independent paths to improved performance - one through networking and inter-unit learning and the other through subsidiary autonomy and innovation. A particular feature of these findings is that they can be shown to be robust after controlling for a wide range of environmental pressures and firm and industry factors. However, in the absence of environmental controls the dual path finding is rejected. These conflicting findings support the imperative to test models that include a diverse range of environmental pressures so that the true effects of organizational factors on learning, innovation and performance can be identified

Subsidiary size and the level of subsidiary autonomy in multinational corporations: a quadratic model investigation of Australian subsidiaries

We investigate the relationship between subsidiary size and subsidiary autonomy in multinational corporations (MNCs) and conclude that a quadratic inverted U-shaped model is the best fit to our data. Founding our arguments in resource dependence theory, we propose that, while the subsidiary is relatively small, increasing subsidiary size will correlate with increasing resources in the subsidiary and a consequent increase in subsidiary autonomy. This positive linear relationship persists until an inflection point is reached and subsidiary autonomy begins to decline. We argue that this is due to increasing subsidiary size bringing increasing coordination complexity, a need for greater inputs of managerial experience and expertise, and growing interdependence between the subsidiary and the rest of the corporation. Employing a sample of 313 Australian subsidiaries of mostly US, UK, European and Japanese MNCs, we use a three-step hierarchical regression to investigate controls only, linear and quadratic effects of subsidiary size on subsidiary autonomy. The quadratic inverted-U model supports and extends Hedlund's (1981) proposition. A post hoc investigation suggested that there might be value in exploring a sinusoidal relationship between size and autonomy. Journal of International Business Studies (2007) 38, 787–801. doi:10.1057/palgrave.jibs.8400294

Cultural Diversity, Strategic Alliance Configurations, and Ecological Innovations of MNEs

© 2020 Palgrave Macmillan. This is a post-peer-review, pre-copyedit version of a chapter published in a book “Non-market Strategies in International Business : How MNEs capture value through their political, social and environmental strategies”. The final authenticated version is available online at: http://dx.doi.org/10.1007/978-3-030-35074-1_10.fi=vertaisarvioitu|en=peerReviewed