How abstract is risk for workers? Expertise, context and introspection in abstract concepts

Two studies were performed to test whether abstract concepts are grounded in experience and activate introspective/linguistic information. In Study 1, four groups of participants, each with different expertise in the domain of safety and security at the workplace (S&S), defined abstract concepts belonging to the S&S domain and differing in degree of abstractness. The definitions included mainly situations, confirming grounding of abstract concepts. In Study 2 the task was performed by students with no experience of S&S. The definitions were modulated by participants’ expertise; the role of introspection increased with more abstract concepts. Results support embodied theories on abstract concept

Text classification supervised algorithms with term frequency inverse document frequency and global vectors for word representation: a comparative study

Over the course of the previous two decades, there has been a rise in the quantity of text documents stored digitally. The ability to organize and categorize those documents in an automated mechanism, is known as text categorization which is used to classify them into a set of predefined categories so they may be preserved and sorted more efficiently. Identifying appropriate structures, architectures, and methods for text classification presents a challenge for researchers. This is due to the significant impact this concept has on content management, contextual search, opinion mining, product review analysis, spam filtering, and text sentiment mining. This study analyzes the generic categorization strategy and examines supervised machine learning approaches and their ability to comprehend complex models and nonlinear data interactions. Among these methods are k-nearest neighbors (KNN), support vector machine (SVM), and ensemble learning algorithms employing various evaluation techniques. Thereafter, an evaluation is conducted on the constraints of every technique and how they can be applied to real-life situations

Alternative Archaeological Representations within Virtual Worlds

Traditional VR methods allow the user to tour and view the virtual world from different perspectives. Increasingly, more interactive and adaptive worlds are being generated, potentially allowing the user to interact with and affect objects in the virtual world. We describe and compare four models of operation that allow the publisher to generate views, with the client manipulating and affecting specific objects in the world. We demonstrate these approaches through a problem in archaeological visualization

A web-based teaching/learning environment to support collaborative knowledge construction in design

A web-based application has been developed as part of a recently completed research which proposed a conceptual framework to collect, analyze and compare different design experiences and to construct structured representations of the emerging knowledge in digital architectural design. The paper introduces the theoretical and practical development of this application as a teaching/learning environment which has significantly contributed to the development and testing of the ideas developed throughout the research. Later in the paper, the application of BLIP in two experimental (design) workshops is reported and evaluated according to the extent to which the application facilitates generation, modification and utilization of design knowledge

Abstraction and context in concept representation

This paper develops the notion of abstraction in the context of the psychology of concepts, and discusses its relation to context dependence in knowledge representation. Three general approaches to modelling conceptual knowledge from the domain of cognitive psychology are discussed, which serve to illustrate a theoretical dimension of increasing levels of abstraction

A Sentimental Education: Sentiment Analysis Using Subjectivity Summarization Based on Minimum Cuts

Sentiment analysis seeks to identify the viewpoint(s) underlying a text span; an example application is classifying a movie review as "thumbs up" or "thumbs down". To determine this sentiment polarity, we propose a novel machine-learning method that applies text-categorization techniques to just the subjective portions of the document. Extracting these portions can be implemented using efficient techniques for finding minimum cuts in graphs; this greatly facilitates incorporation of cross-sentence contextual constraints.Comment: Data available at http://www.cs.cornell.edu/people/pabo/movie-review-data