Abstract. It is considered good software design practice to organize source code into modules and to favour within-module connections (cohesion) over between-module connections (coupling), leading to the oftrepeated maxim "low coupling/high cohesion". Prior research into network theory and its application to software systems has found evidence that many important properties in real software systems exhibit approximately scale-free structure, including coupling; researchers have claimed that such scale-free structures are ubiquitous. This implies that high coupling must be unavoidable, statistically speaking, apparently contradicting standard ideas about software structure. We present a model that leads to the simple predictions that approximately scale-free structures ought to arise both for between-module connectivity and overall connectivity, and not as the result of poor design or optimization shortcuts. These predictions are borne out by our large-scale empirical study. Hence we conclude that high coupling is not avoidable-and that this is in fact quite reasonable.

With 13,000,000 volumes comprising 4.5 billion pages of text, it is currently very difficult for scholars to locate relevant sets of documents that are useful in their research from the HathiTrust Digital Libary (HTDL) using traditional lexically-based retrieval techniques. Existing document search tools and document clustering approaches use purely lexical analysis, which cannot address the inherent ambiguity of natural language. A semantic search approach offers the potential to overcome the shortcoming of lexical search, but-even if an appropriate network of ontologies could be decided upon-it would require a full semantic markup of each document. In this paper, we present a conceptual design and report on the initial implementation of a new framework that affords the benefits of semantic search while minimizing the problems associated with applying existing semantic analysis at scale. Our approach avoids the need for complete semantic document markup using pre-existing ontologies by developing an automatically generated Concept-in-Context (CiC) network seeded by a priori analysis of Wikipedia texts and identification of semantic metadata. Our Capisco system analyzes documents by the semantics and context of their content. The disambiguation of search queries is done interactively, to fully utilize the domain knowledge of the scholar. Our method achieves a form of semantic-enhanced search that simultaneously exploits the proven scale benefits provided by lexical indexing.

A complete text-to-speech system has been created by the authors, based on a tube resonance model of the vocal tract and a development of Carré’s “Distinctive Region Model”, which is in turn based on the formant-sensitivity findings of Fant and Pauli (1974), to control the tube. In order to achieve this goal, significant long-term linguistic research has been involved, including rhythm and intonation studies, as well as the development of low-level articulatory data and rules to drive the model, together with the necessary tools, parsers, dictionaries and so on. The tools and the current system are available under a General Public License, and are described here, with further references in the paper, including samples of the speech produced, and figures illustrating the system description.

This article discusses a new approach to scholarly search and discovery in large-scale text corpora. While lexicographic search is at present the predominant means to access large document corpora, it cannot directly address the inherent ambiguity of natural language. As a pragmatic solution, many scholars manually build their own list of suitable search terms to be used in repeated searches in digital libraries and other online resources; however, scholars then have to resolve on a case-by-case basis issues caused by synonyms, homonyms and OCR errors. Our approach differs from this by supporting scholars in developing and refining a set of relevant concepts, searches a large document collection using semantic concepts, and categorizes the potentially relevant documents from search results into worksets. The developed technique revisits the notion of semantic search and redesigns both the underlying data representation and interface support. This is achieved through an end-to-end design that relies centrally on a Concept-in-Context network sourced through the link structure of Wikipedia. We discuss here the principles of our approach, its implementation in the Capisco prototype, and the relationship between established search techniques and our approach.