Busy as a Bee or Unemployed?: Shifting Scientific Discourse on Work

Changing images of work in discourse both portray and co-constitute the shift from an industrial to a postindustrial economy. Specifically, work metaphors appear in extra-scientific and intra-scientific discourse on workers and work structures in the natural and social world. An analysis of the entomological discourse from the late nineteenth century to the present shows changes in these metaphors that overlap with the discourse of change in human work and organizational structures. For instance, the metaphor of a busy bee within an efficient hive had traditionally evoked a comparison to the modern industrial workplace. The discourse on the hive currently more closely resembles a postindustrial conception of work. Discourse analysis can illustrate the role of language in co-constructing shared changes in image of work.     

The Middle East Books Market: a Personal Overview

A concise review is presented of the essential elements of the book publishing and bookselling business in a complex and diverse market. Demographics create the diversity while Arabic is the one language spoken across a multitude of different countries. Three features are common to all of these countries and they are: the dominance of English as the second language, education has and will remain to be a priority, and economic pressures cause price sensitivity to be a major challenge especially in the educational market segments. It is the price sensitivity as a key factor for the lack of respect for intellectual property and copyright regulations. A review of distribution and retail stores is a very important topic in relation to this complex collection of countries. The emergence of e-books is discussed especially in the educational, professional and reference market segments. Finally, there is a review of the Arabic language publishers and local book fairs to bring this article to closure.     

Finding related sentence pairs in MEDLINE

We explore the feasibility of automatically identifying sentences in different MEDLINE abstracts that are related in meaning. We compared traditional vector space models with machine learning methods for detecting relatedness, and found that machine learning was superior. The Huber method, a variant of Support Vector Machines which minimizes the modified Huber loss function, achieves 73% precision when the score cutoff is set high enough to identify about one related sentence per abstract on average. We illustrate how an abstract viewed in PubMed might be modified to present the related sentences found in other abstracts by this automatic procedure.     

Book Distribution in the Arab World

Business opportunities in international markets are largely determined by accessibility, transparency, and legal frameworks fair to all parties concerned. Arab book markets in principle offer a tremendous potential with more than 300 million readers sharing one common language. Besides unsolved censorship issues, problems with money collection, and differing regulations, however, the lack of a uniform book distribution system is the most critical hurdle for the development of a common Arabic book market. Most importantly, there is no comprehensive database containing accurate content briefs, background information on authors, ISBN numbers, and publisher company information as well as all relevant commercial data such as book prices, weight, logistics terms, and availability. Authors and publishers are hard hit as they often need to organize book transportation, delivery, and invoice collection on their own. At present, “selling book fairs” in the Arab world—with the exception of Abu Dhabi International Book Fair—present themselves as crowded book bazaars rather than executive meeting points. The authors of the article, however, are anticipating change: New distribution initiatives will soon interlink regional Arabic markets and create unique growth and profit opportunities.     

A Look at Global Expansion for e-Books

Forecasts indicate a growing demand for digital content around the world. Converting to digital content can open up countless new revenue possibilities. Adding alternate channels may be worth consideration, but what are the options and what does it take to utilize them effectively? This article explores the opportunities that potentially exist for publishers with content that may be digitized and distributed to global markets. A brief review of the current marketplace is also incorporated, including current players and trends. Finally, an analysis of some of the potential issues, challenges and pitfalls is presented in context of delving into a new international digital content distribution strategy.     

Learning to rank with (a lot of) word features

In this article we present Supervised Semantic Indexing which defines a class of nonlinear (quadratic) models that are discriminatively trained to directly map from the word content in a query-document or document-document pair to a ranking score. Like Latent Semantic Indexing (LSI), our models take account of correlations between words (synonymy, polysemy). However, unlike LSI our models are trained from a supervised signal directly on the ranking task of interest, which we argue is the reason for our superior results. As the query and target texts are modeled separately, our approach is easily generalized to different retrieval tasks, such as cross-language retrieval or online advertising placement. Dealing with models on all pairs of words features is computationally challenging. We propose several improvements to our basic model for addressing this issue, including low rank (but diagonal preserving) representations, correlated feature hashing and sparsification. We provide an empirical study of all these methods on retrieval tasks based on Wikipedia documents as well as an Internet advertisement task. We obtain state-of-the-art performance while providing realistically scalable methods.