Ten Commandments Revisited: A Ten-Year Perspective on the Industrial Application of Formal Methods

Ten years ago, our 1995 paper Ten Commandments of Formal Methods suggested some guidelines to help ensure the success of a formal methods project. It proposed ten important requirements (or "commandments") for formal developers to consider and follow, based on our knowledge of several industrial application success stories, most of which have been reported in more detail in two books. The paper was surprisingly popular, is still widely referenced, and used as required reading in a number of formal methods courses. However, not all have agreed with some of our commandments, feeling that they may not be valid in the long-term. We re-examine the original commandments ten years on, and consider their validity in the light of a further decade of industrial best practice and experiences

Research in the Language, Information and Computation Laboratory of the University of Pennsylvania

This report takes its name from the Computational Linguistics Feedback Forum (CLiFF), an informal discussion group for students and faculty. However the scope of the research covered in this report is broader than the title might suggest; this is the yearly report of the LINC Lab, the Language, Information and Computation Laboratory of the University of Pennsylvania. It may at first be hard to see the threads that bind together the work presented here, work by faculty, graduate students and postdocs in the Computer Science and Linguistics Departments, and the Institute for Research in Cognitive Science. It includes prototypical Natural Language fields such as: Combinatorial Categorial Grammars, Tree Adjoining Grammars, syntactic parsing and the syntax-semantics interface; but it extends to statistical methods, plan inference, instruction understanding, intonation, causal reasoning, free word order languages, geometric reasoning, medical informatics, connectionism, and language acquisition. Naturally, this introduction cannot spell out all the connections between these abstracts; we invite you to explore them on your own. In fact, with this issue it’s easier than ever to do so: this document is accessible on the “information superhighway”. Just call up http://www.cis.upenn.edu/~cliff-group/94/cliffnotes.html In addition, you can find many of the papers referenced in the CLiFF Notes on the net. Most can be obtained by following links from the authors’ abstracts in the web version of this report. The abstracts describe the researchers’ many areas of investigation, explain their shared concerns, and present some interesting work in Cognitive Science. We hope its new online format makes the CLiFF Notes a more useful and interesting guide to Computational Linguistics activity at Penn

AmAMorph: Finite State Morphological Analyzer for Amazighe

This paper presents AmAMorph, a morphological analyzer for Amazighe language using a system based on the NooJ linguistic development environment. The paper begins with the development of Amazighe lexicons with large coverage formalization. The built electronic lexicons, named ‘NAmLex’, ‘VAmLex’ and ‘PAmLex’ which stand for ‘Noun Amazighe Lexicon’, ‘Verb Amazighe Lexicon’ and ‘Particles Amazighe Lexicon’, link inflectional, morphological, and syntacticsemantic information to the list of lemmas. Automated inflectional and derivational routines are applied to each lemma producing over inflected forms. To our knowledge,AmAMorph is the first morphological analyzer for Amazighe. It identifies the component morphemes of the forms using large coverage morphological grammars. Along with the description of how the analyzer is implemented, this paper gives an evaluation of the analyzer

Formal Linguistic Models and Knowledge Processing. A Structuralist Approach to Rule-Based Ontology Learning and Population

2013 - 2014The main aim of this research is to propose a structuralist approach for knowledge processing by means of ontology learning and population, achieved starting from unstructured and structured texts. The method suggested includes distributional semantic approaches and NL formalization theories, in order to develop a framework, which relies upon deep linguistic analysis... [edited by author]XIII n.s

A theory and model for the evolution of software services

Software services are subject to constant change and variation. To control service development, a service developer needs to know why a change was made, what are its implications and whether the change is complete. Typically, service clients do not perceive the upgraded service immediately. As a consequence, service-based applications may fail on the service client side due to changes carried out during a provider service upgrade. In order to manage changes in a meaningful and effective manner service clients must therefore be considered when service changes are introduced at the service provider's side. Otherwise such changes will most certainly result in severe application disruption. Eliminating spurious results and inconsistencies that may occur due to uncontrolled changes is therefore a necessary condition for the ability of services to evolve gracefully, ensure service stability, and handle variability in their behavior. Towards this goal, this work presents a model and a theoretical framework for the compatible evolution of services based on well-founded theories and techniques from a number of disparate fields.

Making AI meaningful again

Artificial intelligence (AI) research enjoyed an initial period of enthusiasm in the 1970s and 80s. But this enthusiasm was tempered by a long interlude of frustration when genuinely useful AI applications failed to be forthcoming. Today, we are experiencing once again a period of enthusiasm, fired above all by the successes of the technology of deep neural networks or deep machine learning. In this paper we draw attention to what we take to be serious problems underlying current views of artificial intelligence encouraged by these successes, especially in the domain of language processing. We then show an alternative approach to language-centric AI, in which we identify a role for philosophy.Comment: 23 pages, 1 Tabl