Model-Driven Software Development (MDSD) is currently a highly regarded development paradigm among developers and researchers. With the advent of OMG's MDA and Microsoft's Software Factories, the MDSD approach has moved to the centre of the programmer's attention, becoming the focus of conferences such as OOPSLA, JAOO and OOP. MDSD is about using domain-specific languages to create models that express application structure or behaviour in an efficient and domain-specific way. These models are subsequently transformed into executable code by a sequence of model transformations. This practical guide for software architects and developers is peppered with practical examples and extensive case studies. International experts deliver: * A comprehensive overview of MDSD and how it relates to industry standards such as MDA and Software Factories. * Technical details on meta modeling, DSL construction, model-to-model and model-to-code transformations, and software architecture. * Invaluable insight into the software development process, plus engineering issues such as versioning, testing and product line engineering. * Essential management knowledge covering economic and organizational topics, from a global perspective. Get started and benefit from some practical support along the way!

Machine translation (MT) is the area of computer science and applied linguistics dealing with the translation of human languages such as English and German. MT on the Internet has become an important tool by providing fast, economical and useful translations. With globalisation and expanding trade, demand for translation is set to grow. Translation Engines covers theoretical and practical aspects of MT, both classic and new, including: - Character sets and formatting languages - Translation memory - Linguistic and computational foundations - Basic computational linguistic techniques - Transfer and interlingua MT - Evaluation Software accompanies the text, providing readers with hands on experience of the main algorithms.

Spoken language understanding (SLU) is an emerging field in between speech and language processing, investigating human/ machine and human/ human communication by leveraging technologies from signal processing, pattern recognition, machine learning and artificial intelligence. SLU systems are designed to extract the meaning from speech utterances and its applications are vast, from voice search in mobile devices to meeting summarization, attracting interest from both commercial and academic sectors. Both human/machine and human/human communications can benefit from the application of SLU, using differing tasks and approaches to better understand and utilize such communications. This book covers the state-of-the-art approaches for the most popular SLU tasks with chapters written by well-known researchers in the respective fields. Key features include: Presents a fully integrated view of the two distinct disciplines of speech processing and language processing for SLU tasks. Defines what is possible today for SLU as an enabling technology for enterprise (e.g., customer care centers or company meetings), and consumer (e.g., entertainment, mobile, car, robot, or smart environments) applications and outlines the key research areas. Provides a unique source of distilled information on methods for computer modeling of semantic information in human/machine and human/human conversations. This book can be successfully used for graduate courses in electronics engineering, computer science or computational linguistics. Moreover, technologists interested in processing spoken communications will find it a useful source of collated information of the topic drawn from the two distinct disciplines of speech processing and language processing under the new area of SLU.

Risto Miikkulainen draws on recent connectionist work in language comprehension tocreate a model that can understand natural language. Using the DISCERN system as an example, hedescribes a general approach to building high-level cognitive models from distributed neuralnetworks and shows how the special properties of such networks are useful in modeling humanperformance. In this approach connectionist networks are not only plausible models of isolatedcognitive phenomena, but also sufficient constituents for complete artificial intelligencesystems.Distributed neural networks have been very successful in modeling isolated cognitivephenomena, but complex high-level behavior has been tractable only with symbolic artificialintelligence techniques. Aiming to bridge this gap, Miikkulainen describes DISCERN, a completenatural language processing system implemented entirely at the subsymbolic level. In DISCERN,distributed neural network models of parsing, generating, reasoning, lexical processing, andepisodic memory are integrated into a single system that learns to read, paraphrase, and answerquestions about stereotypical narratives.Miikkulainen's work, which includes a comprehensive surveyof the connectionist literature related to natural language processing, will prove especiallyvaluable to researchers interested in practical techniques for high-level representation,inferencing, memory modeling, and modular connectionist architectures.Risto Miikkulainen is anAssistant Professor in the Department of Computer Sciences at The University of Texas atAustin.

Studies in Computational Linguistics presents authoritative texts from an international team of leading computational linguists. The books range from the senior undergraduate textbook to the research level monograph and provide a showcase for a broad range of recent developments in the field. The series should be interesting reading for researchers and students alike involved at this interface of linguistics and computing.

Drawing upon tense and aspect theories, temporal logics, and temporal databases, this cross-discipline book examines relevant issues from the three areas, developing a unified theoretical framework that can be used to build natural language interfaces to temporal databases.