This book constitutes the refereed proceedings of the 20th International Conference on Applications of Natural Language to Information Systems, NLDB 2015, held in Passau, Germany, in June 2015. The 18 full papers, 15 short papers, 14 poster and demonstration papers presented were carefully reviewed and selected from 100 submissions. The papers cover the following topics: information extraction, distributional semantics, querying and question answering systems, context-aware NLP, cognitive and semantic computing, sentiment and opinion analysis, information extraction and social media, NLP and usability, text classification and extraction, and posters and demonstrations.

The symposium on which this volume was based brought together approximately fifty scientists from a variety of backgrounds to discuss the rapidly-emerging set of competing technologies for exploiting a massive quantity of textual information. This group was challenged to explore new ways to take advantage of the power of on-line text. A billion words of text can be more generally useful than a few hundred logical rules, if advanced computation can extract useful information from streams of text and help find what is needed in the sea of available material. While the extraction task is a hot topic for the field of natural language processing and the retrieval task is a solid aspect in the field of information retrieval, these two disciplines came together at the symposium and have been cross-breeding more than ever. The book is organized in three parts. The first group of papers describes the current set of natural language processing techniques used for interpreting and extracting information from quantities of text. The second group gives some of the historical perspective, methodology, and current practice of information retrieval work; the third covers both current and emerging applications of these techniques. This collection of readings should give students and scientists alike a good idea of the current techniques as well as a general concept of how to go about developing and testing systems to handle volumes of text.

The papers in this volume are from the Ninth International Conference on Design Computing and Cognition (DCC’20) held virtually at the Georgia Institute of Technology, Atlanta, USA. They represent the state-of-the-art of research and development in design computing and design cognition including the increasingly active area of design cognitive neuroscience. They are of particular interest to design researchers, developers and users of advanced computation in designing as well as to design educators. This volume contains knowledge about the cognitive behavior of designers, which is valuable for those who need to gain a better understanding of designing.

First published in 1996, this collection of essays by distinguished computer scientists celebrates the achievements of research and speculates about the unsolved problems in computer science that require future investigation. Since the subject stretches from technology in the field, through engineering design to foundations in mathematics, there is a wide variety of concerns and approaches among the authors. The book's purpose is to show that long-term research in computer science is crucial and that it must not be driven solely by commercial considerations. The authors do not shirk the difficult aspects of their topics, but try to expose them in the simplest terms possible without diluting them, in order that the reader can understand the issues involved. Thus the book also represents a broad overview of much of the state of knowledge and future expectations of computer science, illustrating that it is much more than a technology and it is a fully fledged and growing intellectual discipline with its own engineering principles and its own scientific concepts and models. It will be stimulating reading because it represents the views of prominent authorities who have had a significant impact on the direction of innovation, research and development in computer science.

Unlike humans, computers generally do not take their peers in communication into account. Adding to this the increasing complexity of information systems, the need for adaptive personalisation is there. In this thesis we look at adaptive systems from the perspective of interactive systems. As most systems are, or can be seen as, interactive systems this should pose no problem. In interactive systems users cause events. These events can be passed on to an adaptation system to maintain a user model. The events also cause the interactive system to react. These reactions may be parameterised by the user model. In this thesis the following research questions are addressed: * How can adaptive personalisation be integrated into user adaptive systems? * How can adaptive personalisation be evaluated? To answer these questions it is essential to first provide a model of user adaptive systems. We introduce the Generic Adaptivity Model (GAM). The GAM divides the system into four layers: the application layer, the interface layer, the reasoning layer and the user model layer. It is important to notice that the reasoning layer consists of two reasoning components: the push adaptation component and the pull adaptation component. The push adaptation component is responsible for transforming user events into user model updates. As such it maintains the user model and the reasoning happens when users perform events. It is not necessary that these updates have been completed for the application to react to the user events. The pull adaptation component is responsible to using the user model to answer questions about the user that influence the system reaction to the user. As such this is computed at the moment a reaction is required and is more time critical than push reasoning. The behaviour of an adaptation component largely standard. As such it makes sense to create an adaptation engine that can be used in conjunction with an adaptation description to implement the adaptation component. The adaptation description then describes, by means of a script language, the push and pull reasoning to be performed as well as the events and questions to be recognised. Related elements in an adaptation model can be grouped together into an adaptation element. Together all adaptation elements in an adaptation model form an adaptation graph. This dependency graph can be used to visualise an adaptation model. In evaluating adaptation models the final evaluation involves testing with users. There are however two other evaluation layers that are less costly. The first evaluation layer involves a rough evaluation on the kind of reasoning used (push or pull). The second layer performs a detailed structural analysis of an adaptation model. The evaluation layers work on a number of dimensions. These dimensions are: predictability, adaptability, supportability, control, speed, extensibility, model size, privacy, concurrency and prediction quality. In the structural evaluation level a number of indicators are used for each dimension. Looking at the GAM it has a number of benefits: * It will allow different applications to cooperatively maintain properties by using common names and merging adaptation models. * It has strong capabilities for ensuring privacy and user control over the user models. * By cooperative modelling more information be used to have more effective personalisations. * The model is very generic and does not prescribe reasoning models. As such it is broadly applicable. * The model coexists well with the evaluation framework and does not violate any dimension. To answer the question how to integrate adaptive personalisation we introduce a seven stage method for creating adaptation models. In the first step the application is analysed. In the second step possible personalisation opportunities are determined. In the third step questions about the user are found. In the fourth step the user properties are determined. The fifth step determines the events needed to maintain the user model. The sixth step combines the results and cleans out infeasible options. Finally the seventh step evaluates the options to select only the best opportunities for adaptive personalisation.

This book constitutes revised and selected papers from the scientific satellite events held in conjunction with the18th International Conference on Service-Oriented Computing, ICSOC 2020. The conference was held virtually during December 14-17, 2020. A total of 125 submissions were received for the satellite events. The volume includes 9 papers from the PhD Symposium Track, 4 papers from the Demonstration Track, and 45 papers from the following workshops: International Workshop on Artificial Intelligence for IT Operations (AIOps) International Workshop on Cyber Forensics and Threat Investigations Challenges in Emerging Infrastructures (CFTIC 2020) 2nd Workshop on Smart Data Integration and Processing (STRAPS 2020) International Workshop on AI-enabled Process Automation (AI-PA 2020) International Workshop on Artificial Intelligence in the IoT Security Services (AI-IOTS 2020)

This two-volume set of LNAI 13028 and LNAI 13029 constitutes the refereed proceedings of the 10th CCF Conference on Natural Language Processing and Chinese Computing, NLPCC 2021, held in Qingdao, China, in October 2021. The 66 full papers, 23 poster papers, and 27 workshop papers presented were carefully reviewed and selected from 446 submissions. They are organized in the following areas: Fundamentals of NLP; Machine Translation and Multilinguality; Machine Learning for NLP; Information Extraction and Knowledge Graph; Summarization and Generation; Question Answering; Dialogue Systems; Social Media and Sentiment Analysis; NLP Applications and Text Mining; and Multimodality and Explainability.