8,389 research outputs found
Exploratory topic modeling with distributional semantics
As we continue to collect and store textual data in a multitude of domains,
we are regularly confronted with material whose largely unknown thematic
structure we want to uncover. With unsupervised, exploratory analysis, no prior
knowledge about the content is required and highly open-ended tasks can be
supported. In the past few years, probabilistic topic modeling has emerged as a
popular approach to this problem. Nevertheless, the representation of the
latent topics as aggregations of semi-coherent terms limits their
interpretability and level of detail.
This paper presents an alternative approach to topic modeling that maps
topics as a network for exploration, based on distributional semantics using
learned word vectors. From the granular level of terms and their semantic
similarity relations global topic structures emerge as clustered regions and
gradients of concepts. Moreover, the paper discusses the visual interactive
representation of the topic map, which plays an important role in supporting
its exploration.Comment: Conference: The Fourteenth International Symposium on Intelligent
Data Analysis (IDA 2015
Discover semantic topics in patents within a specific domain
© Rinton Press. Patent topic discovery is critical for innovation-oriented enterprises to hedge the patent application risks and raise the success rate of patent application. Topic models are commonly recognized as an efficient tool for this task by researchers from both academy and industry. However, many existing well-known topic models, e.g., Latent Dirichlet Allocation (LDA), which are particularly designed for the documents represented by word-vectors, exhibit low accuracy and poor interpretability on patent topic discovery task. The reason is that 1) the semantics of documents are still under-explored in a specific domain 2) and the domain background knowledge is not successfully utilized to guide the process of topic discovery. In order to improve the accuracy and the interpretability, we propose a new patent representation and organization with additional inter-word relationships mined from title, abstract, and claim of patents. The representation can endow each patent with more semantics than word-vector. Meanwhile, we build a Backbone Association Link Network (Backbone ALN) to incorporate domain background semantics to further enhance the semantics of patents. With new semantic-rich patent representations, we propose a Semantic LDA model to discover semantic topics from patents within a specific domain. It can discover semantic topics with association relations between words rather than a single word vector. At last, accuracy and interpretability of the proposed model are verified on real-world patents datasets from the United States Patent and Trademark Office. The experimental results show that Semantic LDA model yields better performance than other conventional models (e.g., LDA). Furthermore, our proposed model can be easily generalized to other related text mining corpus
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Twenty years of technology and strategic roadmapping research: A school of thought perspective
© 2020 Two decades ago the thirtieth-anniversary special issue of Technological Forecasting and Social Change correctly anticipated the widespread adoption of technology and strategic roadmapping at firm, sectoral and national levels. In this article, we explore the evolution of roadmapping studies since that time. Drawing on a mixed-methods approach (i.e. topic modelling, genealogical analysis, content analysis and interviews), we reveal the development of seven distinctive ‘schools of thought’: the Cambridge practical school, the Seoul school, the Portland and Bangkok schools, the Cambridge phenomenological school, the Beijing school and the Moscow school. We show that the schools differ in terms of (a) the research orientation, whether it be solution- or theory-oriented; (b) the research methods and data sources being used; and (c) the nature of contributions that each school seeks to achieve. The different areas of emphasis associated with each school are not competing but complementary, and together they develop the eclectic body of knowledge on roadmapping
Machine Learning in Management Accounting Research : Literature Review and Pathways for the Future
This paper explores the possibilities of employing machine learning (ML) methods and new data sources in management accounting (MA) research. A review of current accounting and related research reveals that ML methods in MA are still in their infancy. However, a review of recently published ML research from related fields reveals several new opportunities to utilize ML in MA research. We suggest that the most promising areas to employ ML methods in MA research lie in (1) the exploitation of the rich potential of various textual data sources; (2) the quantification of qualitative and unstructured data to create new measures; (3) the creation of better estimates and predictions; and (4) the use of explainable AI to interpret ML models in detail. ML methods can play a crucial role in MA research by creating, developing, and refining theories through induction and abduction, as well as by providing tools for interventionist studies.© 2022 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group
This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives License (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited, and is not altered, transformed, or built upon in any way.fi=vertaisarvioitu|en=peerReviewed
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Essays on the Economics of Technological Change and the Environment
Technological change bears the promise of addressing environmental problems without reneging on economic development. However, taping its full potential requires an understanding of its drivers and barriers. The three chapters of this dissertation are a modest attempt at casting light on some of the factors that can foster technological change towards more environmental-friendly technologies. In Chapter One, I provide the first quantitative evidence that the Montreal Protocol, and its following amendments to protect the ozone layer, triggered a large increase in research and innovation on alternatives to ozone-depleting molecules. To do this, I use the full text of patents and scientific articles and implement a difference-in-differences strategy and a synthetic control method. To compare molecules’ chemical and industrial characteristics, I construct descriptive variables by applying machine learning techniques to the documents’ text. In Chapter Two, I investigate barriers to adopting solar lanterns in the context of rural Indian households. I design and implement a randomized controlled trial on people’s willingness to pay for such lanterns, and find that, despite the relative simplicity of the product, information barriers to adopting solar lanterns remain high. Chapter Three theoretically investigates firm-level barriers to green technological change. I outline a mechanism that explains why coordination at the industry level might be necessary. I argue that radical innovations (such as electric cars) require complementary innovations in interdependent components, and show that, when technological change requires investment by both suppliers and producers, coordination within an industry is needed and can be difficult to obtain
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