Monolingual Sentence Rewriting as Machine Translation: Generation and Evaluation

In this thesis, we investigate approaches to paraphrasing entire sentences within the constraints of a given task, which we call monolingual sentence rewriting. We introduce a unified framework for monolingual sentence rewriting, and apply it to three representative tasks: sentence compression, text simplification, and grammatical error correction. We also perform a detailed analysis of the evaluation methodologies for each task, identify bias in common evaluation techniques, and propose more reliable practices. Monolingual rewriting can be thought of as translating between two types of English (such as from complex to simple), and therefore our approach is inspired by statistical machine translation. In machine translation, a large quantity of parallel data is necessary to model the transformations from input to output text. Parallel bilingual data naturally occurs between common language pairs (such as English and French), but for monolingual sentence rewriting, there is little existing parallel data and annotation is costly. We modify the statistical machine translation pipeline to harness monolingual resources and insights into task constraints in order to drastically diminish the amount of annotated data necessary to train a robust system. Our method generates more meaning-preserving and grammatical sentences than earlier approaches and requires less task-specific data. Once candidate sentences are generated, it is crucial to have reliable evaluation methods. Sentential paraphrases must fulfill a variety of requirements: preserve the meaning of the original sentence, be grammatical, and meet any stylistic or task-specific constraints. We analyze common evaluation practices and propose better methods that more accurately measure the quality of output. Often overlooked, robust automatic evaluation methodology is necessary for improving systems, and this work presents new metrics and outlines important considerations for reliably measuring the quality of the generated text

Essays on Angel Investing in the Entrepreneurial Ecosystem

Throughout its three chapters, this dissertation examines a phenomenon that, although underappreciated and underinvestigated in the existing literature, should be of great interest to entrepreneurship scholars: angel investing in the United States. While most of the existing studies of venture financing have predominantly focused on venture capital (VC) funding, angel investing—that is, wealthy individuals investing their own money in new ventures—represents almost as large of a market as venture capital, and recent empirical evidence suggests that ventures financed by angel investors tend to be more successful than comparable ventures that are not angel-financed. More interestingly, perhaps, angel investing tends to focus on ventures at the earliest stage, which leads to investor making decisions based on very little hard evidence. This results in the attempt, on the investors’ part, to reduce uncertainty by leveraging one’s connections and community-level patterns of social relations. In this regard, this dissertation’s main objective is perhaps to tackle the existing literature’s “undersocialized” take on venture financing, and to show the sociological mechanisms that might underpin the decision by entrepreneurs to enter the angel investing market by becoming suppliers of capital, as well as their capital allocation choices, i.e. their investment decisions. Additionally, this work also examines the drivers of success for angel investors, with a view to explaining—at least in part—why certain individuals are wildly more successful than others at angel investing. Empirically, my work relies on a combination of archival data—primarily data gathered from online data source CrunchBase, but also U.S. Census data and hand-collected information from LinkedIn—and fieldwork in the form of interviews with entrepreneurs and angel investors, as well as participant observation at the Angel Capital Association Annual Meeting in San Francisco, the largest yearly gathering of angel investors. The resulting empirical patterns, both qualitative and quantitative, when taken in their entirety suggests that angel investing is a social process, and particularly that entrepreneurs are socialized into becoming angel investors by interacting with the angels who finance their ventures. Further, this work offers evidence that community-level patterns of socialization—i.e. what is generally known in sociology as community social capital—also plays a role in determining whether entrepreneurs will become angel investors and, once they choose to take this step, whether they will show a preference for financing local ventures vis-à-vis pursuing investment opportunities elsewhere. Finally, this work also addresses the question of angel investing outcomes—that is, why some angel investors are more successful than others, as measured by the number of exits in their investment portfolio. In this regard, empirical results suggest that generalists do better than specialists, and that angel investors with broad entrepreneurial experience are found to do especially well. Success is also a function of effective knowledge translation: on average, successful entrepreneurs tend to become more successful angels, and especially so the greater the overlap between the entrepreneurial experience of the founder and their angel investment portfolio

The impact of emotions on student participation in an assessed, online, collaborative activity

There is growing recognition of the importance of emotions in academic online learning contexts. However, there is still little known about the role of emotions in social and collaborative online learning settings, especially the relationship between emotions and student participation. To explore this relationship, this study used a prospective longitudinal research design to follow 46 distance learning students throughout a 3-week assessed, online, collaborative activity. This approach allowed the fluctuating and dynamic aspects of emotions to be explored as well as the relationship between emotions and student participation in the collaborative activity. Self-report data were gathered using a semistructured online diary at five time points throughout the task (once at the start of the collaborative activity, three times during the activity, and the final entry after the activity had finished). Findings revealed that learners generally perceived pleasant emotions (such as relief, satisfaction and enjoyment) to have positive impacts, or no impact, on participation, whereas unpleasant emotions (such as anxiety, frustration, and disappointment) were generally perceived to have negative impacts, or no impact, on participation. Interestingly, however, anxiety, and to a smaller extent frustration, were perceived by a number of students to have positive impacts during the activity. To conclude this paper, implications for educators are highlighted

Forecasting: theory and practice

Forecasting has always been in the forefront of decision making and planning. The uncertainty that surrounds the future is both exciting and challenging, with individuals and organisations seeking to minimise risks and maximise utilities. The lack of a free-lunch theorem implies the need for a diverse set of forecasting methods to tackle an array of applications. This unique article provides a non-systematic review of the theory and the practice of forecasting. We offer a wide range of theoretical, state-of-the-art models, methods, principles, and approaches to prepare, produce, organise, and evaluate forecasts. We then demonstrate how such theoretical concepts are applied in a variety of real-life contexts, including operations, economics, finance, energy, environment, and social good. We do not claim that this review is an exhaustive list of methods and applications. The list was compiled based on the expertise and interests of the authors. However, we wish that our encyclopedic presentation will offer a point of reference for the rich work that has been undertaken over the last decades, with some key insights for the future of the forecasting theory and practice

Forecasting: theory and practice

Forecasting has always been at the forefront of decision making and planning. The uncertainty that surrounds the future is both exciting and challenging, with individuals and organisations seeking to minimise risks and maximise utilities. The large number of forecasting applications calls for a diverse set of forecasting methods to tackle real-life challenges. This article provides a non-systematic review of the theory and the practice of forecasting. We provide an overview of a wide range of theoretical, state-of-the-art models, methods, principles, and approaches to prepare, produce, organise, and evaluate forecasts. We then demonstrate how such theoretical concepts are applied in a variety of real-life contexts. We do not claim that this review is an exhaustive list of methods and applications. However, we wish that our encyclopedic presentation will offer a point of reference for the rich work that has been undertaken over the last decades, with some key insights for the future of forecasting theory and practice. Given its encyclopedic nature, the intended mode of reading is non-linear. We offer cross-references to allow the readers to navigate through the various topics. We complement the theoretical concepts and applications covered by large lists of free or open-source software implementations and publicly-available databases.info:eu-repo/semantics/publishedVersio

Forecasting: theory and practice

Forecasting has always been at the forefront of decision making and planning. The uncertainty that surrounds the future is both exciting and challenging, with individuals and organisations seeking to minimise risks and maximise utilities. The large number of forecasting applications calls for a diverse set of forecasting methods to tackle real-life challenges. This article provides a non-systematic review of the theory and the practice of forecasting. We provide an overview of a wide range of theoretical, state-of-the-art models, methods, principles, and approaches to prepare, produce, organise, and evaluate forecasts. We then demonstrate how such theoretical concepts are applied in a variety of real-life contexts. We do not claim that this review is an exhaustive list of methods and applications. However, we wish that our encyclopedic presentation will offer a point of reference for the rich work that has been undertaken over the last decades, with some key insights for the future of forecasting theory and practice. Given its encyclopedic nature, the intended mode of reading is non-linear. We offer cross-references to allow the readers to navigate through the various topics. We complement the theoretical concepts and applications covered by large lists of free or open-source software implementations and publicly-available databases

Knowledge and Management Models for Sustainable Growth

In the last years sustainability has become a topic of global concern and a key issue in the strategic agenda of both business organizations and public authorities and organisations. Significant changes in business landscape, the emergence of new technology, including social media, the pressure of new social concerns, have called into question established conceptualizations of competitiveness, wealth creation and growth. New and unaddressed set of issues regarding how private and public organisations manage and invest their resources to create sustainable value have brought to light. In particular the increasing focus on environmental and social themes has suggested new dimensions to be taken into account in the value creation dynamics, both at organisations and communities level. For companies the need of integrating corporate social and environmental responsibility issues into strategy and daily business operations, pose profound challenges, which, in turn, involve numerous processes and complex decisions influenced by many stakeholders. Facing these challenges calls for the creation, use and exploitation of new knowledge as well as the development of proper management models, approaches and tools aimed to contribute to the development and realization of environmentally and socially sustainable business strategies and practices

Multidisciplinary Aspects of Design. Objects, Processes, Experiences and Narratives

The book addresses the contemporary perspectives of design on a multidisciplinary through 4 key words: objects, processes, experiences, narratives. It aims at further investigating the role of the archive for the design culture reflecting on “Memory and Future” and “The Tools of Design and the Language of Representation”, and also themes that are yet at the center of the multidisciplinary debate on design. The tenets of the conference (OPEN: objects, processes, experiences and narratives) will hence also correspond to the book sections: -Objects. Design as focused on the object, on its functional and symbolic dimension, and at the same time on the object as a tool for representing cultures; -Processes. The designer’s self-reflective moment which is focused on the analysis and on the definition of processes in various contexts, spanning innovation, social engagement, reflection on emergencies or forecasting. -Experiences. Design as a theoretical and practical strategy aimed at facilitating experiential interactions among people, people and objects or environments. -Narratives. Making history, representing through different media, archiving, narrating, and exhibiting design

Empire and Politics in the Eastern and Western Civilizations

The volume includes the proceedings of the 2nd Roma Sinica conference held in 2019 and aims to compare some features of the ancient political thought in the Western classical tradition and in the Eastern ancient thought. The contributors propose new patterns of interpretation of the mutual interactions and proximities between these two cultural worlds and offer also a perspective of continuity between contemporary and ancient political thought